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Created May 10, 2025 16:57
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyP/R7AzUNmTOtPfaBhV6e52",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/franfj/c212f8669c1f25caa47b00e8ecab0d30/exist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-llww99Jl1iD"
},
"source": [
"### Import Stanza dependencies and models"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G2NY8Rfpi4uu",
"outputId": "f2e07b33-a916-43b4-cc6d-407106b44575"
},
"source": [
"!pip install stanza\n",
"!pip install spacy_stanza\n",
"\n",
"import stanza\n",
"\n",
"# Download english and spanish models\n",
"stanza.download('en')\n",
"stanza.download('es')\n",
"\n",
"# Prepare pipelines\n",
"stanza_nlp_EN = stanza.Pipeline('en')\n",
"stanza_nlp_ES = stanza.Pipeline('es')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting stanza\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/50/ae/a70a58ce6b4e2daad538688806ee0f238dbe601954582a74ea57cde6c532/stanza-1.2-py3-none-any.whl (282kB)\n",
"\u001b[K |████████████████████████████████| 286kB 3.1MB/s \n",
"\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from stanza) (2.23.0)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from stanza) (3.12.4)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from stanza) (1.19.5)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from stanza) (4.41.1)\n",
"Requirement already satisfied: torch>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from stanza) (1.8.1+cu101)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (3.0.4)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (2020.12.5)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (1.24.3)\n",
"Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf->stanza) (1.15.0)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->stanza) (56.1.0)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.3.0->stanza) (3.7.4.3)\n",
"Installing collected packages: stanza\n",
"Successfully installed stanza-1.2\n",
"Collecting spacy_stanza\n",
" Downloading https://files.pythonhosted.org/packages/0b/df/a65f452d679a042d6a4682094ce88c1a1971993d2c2671a34c1bab22b086/spacy_stanza-1.0.0-py3-none-any.whl\n",
"Requirement already satisfied: stanza<1.3.0,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy_stanza) (1.2)\n",
"Collecting spacy<4.0.0,>=3.0.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/1b/d8/0361bbaf7a1ff56b44dca04dace54c82d63dad7475b7d25ea1baefafafb2/spacy-3.0.6-cp37-cp37m-manylinux2014_x86_64.whl (12.8MB)\n",
"\u001b[K |████████████████████████████████| 12.8MB 520kB/s \n",
"\u001b[?25hRequirement already satisfied: torch>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from stanza<1.3.0,>=1.2.0->spacy_stanza) (1.8.1+cu101)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from stanza<1.3.0,>=1.2.0->spacy_stanza) (3.12.4)\n",
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"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from stanza<1.3.0,>=1.2.0->spacy_stanza) (2.23.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from stanza<1.3.0,>=1.2.0->spacy_stanza) (1.19.5)\n",
"Collecting catalogue<2.1.0,>=2.0.3\n",
" Downloading https://files.pythonhosted.org/packages/9c/10/dbc1203a4b1367c7b02fddf08cb2981d9aa3e688d398f587cea0ab9e3bec/catalogue-2.0.4-py3-none-any.whl\n",
"Collecting pathy>=0.3.5\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/13/87/5991d87be8ed60beb172b4062dbafef18b32fa559635a8e2b633c2974f85/pathy-0.5.2-py3-none-any.whl (42kB)\n",
"\u001b[K |████████████████████████████████| 51kB 6.0MB/s \n",
"\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<4.0.0,>=3.0.0->spacy_stanza) (56.1.0)\n",
"Requirement already satisfied: typing-extensions<4.0.0.0,>=3.7.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from spacy<4.0.0,>=3.0.0->spacy_stanza) (3.7.4.3)\n",
"Collecting srsly<3.0.0,>=2.4.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/c3/84/dfdfc9f6f04f6b88207d96d9520b911e5fec0c67ff47a0dea31ab5429a1e/srsly-2.4.1-cp37-cp37m-manylinux2014_x86_64.whl (456kB)\n",
"\u001b[K |████████████████████████████████| 460kB 39.5MB/s \n",
"\u001b[?25hRequirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0.0,>=3.0.0->spacy_stanza) (3.0.5)\n",
"Collecting typer<0.4.0,>=0.3.0\n",
" Downloading https://files.pythonhosted.org/packages/90/34/d138832f6945432c638f32137e6c79a3b682f06a63c488dcfaca6b166c64/typer-0.3.2-py3-none-any.whl\n",
"Collecting pydantic<1.8.0,>=1.7.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ca/fa/d43f31874e1f2a9633e4c025be310f2ce7a8350017579e9e837a62630a7e/pydantic-1.7.4-cp37-cp37m-manylinux2014_x86_64.whl (9.1MB)\n",
"\u001b[K |████████████████████████████████| 9.1MB 38.6MB/s \n",
"\u001b[?25hCollecting thinc<8.1.0,>=8.0.3\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/61/87/decceba68a0c6ca356ddcb6aea8b2500e71d9bc187f148aae19b747b7d3c/thinc-8.0.3-cp37-cp37m-manylinux2014_x86_64.whl (1.1MB)\n",
"\u001b[K |████████████████████████████████| 1.1MB 19.4MB/s \n",
"\u001b[?25hRequirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0.0,>=3.0.0->spacy_stanza) (1.0.5)\n",
"Requirement already satisfied: wasabi<1.1.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0.0,>=3.0.0->spacy_stanza) (0.8.2)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0.0,>=3.0.0->spacy_stanza) (20.9)\n",
"Collecting spacy-legacy<3.1.0,>=3.0.4\n",
" Downloading https://files.pythonhosted.org/packages/8d/67/d4002a18e26bf29b17ab563ddb55232b445ab6a02f97bf17d1345ff34d3f/spacy_legacy-3.0.5-py2.py3-none-any.whl\n",
"Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0.0,>=3.0.0->spacy_stanza) (0.4.1)\n",
"Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0.0,>=3.0.0->spacy_stanza) (2.0.5)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0.0,>=3.0.0->spacy_stanza) (2.11.3)\n",
"Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf->stanza<1.3.0,>=1.2.0->spacy_stanza) (1.15.0)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->stanza<1.3.0,>=1.2.0->spacy_stanza) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->stanza<1.3.0,>=1.2.0->spacy_stanza) (2020.12.5)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->stanza<1.3.0,>=1.2.0->spacy_stanza) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->stanza<1.3.0,>=1.2.0->spacy_stanza) (1.24.3)\n",
"Requirement already satisfied: zipp>=0.5; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.3->spacy<4.0.0,>=3.0.0->spacy_stanza) (3.4.1)\n",
"Collecting smart-open<4.0.0,>=2.2.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/11/9a/ba2d5f67f25e8d5bbf2fcec7a99b1e38428e83cb715f64dd179ca43a11bb/smart_open-3.0.0.tar.gz (113kB)\n",
"\u001b[K |████████████████████████████████| 122kB 44.9MB/s \n",
"\u001b[?25hRequirement already satisfied: click<7.2.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.4.0,>=0.3.0->spacy<4.0.0,>=3.0.0->spacy_stanza) (7.1.2)\n",
"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy<4.0.0,>=3.0.0->spacy_stanza) (2.4.7)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy<4.0.0,>=3.0.0->spacy_stanza) (2.0.1)\n",
"Building wheels for collected packages: smart-open\n",
" Building wheel for smart-open (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for smart-open: filename=smart_open-3.0.0-cp37-none-any.whl size=107098 sha256=07220948a85eac6ff7112b7f2103de1b9aa07dfb4f6da23d2dd8ebe2a05db803\n",
" Stored in directory: /root/.cache/pip/wheels/18/88/7c/f06dabd5e9cabe02d2269167bcacbbf9b47d0c0ff7d6ebcb78\n",
"Successfully built smart-open\n",
"Installing collected packages: catalogue, typer, smart-open, pathy, srsly, pydantic, thinc, spacy-legacy, spacy, spacy-stanza\n",
" Found existing installation: catalogue 1.0.0\n",
" Uninstalling catalogue-1.0.0:\n",
" Successfully uninstalled catalogue-1.0.0\n",
" Found existing installation: smart-open 5.0.0\n",
" Uninstalling smart-open-5.0.0:\n",
" Successfully uninstalled smart-open-5.0.0\n",
" Found existing installation: srsly 1.0.5\n",
" Uninstalling srsly-1.0.5:\n",
" Successfully uninstalled srsly-1.0.5\n",
" Found existing installation: thinc 7.4.0\n",
" Uninstalling thinc-7.4.0:\n",
" Successfully uninstalled thinc-7.4.0\n",
" Found existing installation: spacy 2.2.4\n",
" Uninstalling spacy-2.2.4:\n",
" Successfully uninstalled spacy-2.2.4\n",
"Successfully installed catalogue-2.0.4 pathy-0.5.2 pydantic-1.7.4 smart-open-3.0.0 spacy-3.0.6 spacy-legacy-3.0.5 spacy-stanza-1.0.0 srsly-2.4.1 thinc-8.0.3 typer-0.3.2\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/master/resources_1.2.0.json: 128kB [00:00, 39.2MB/s] \n",
"2021-05-31 21:34:48 INFO: Downloading default packages for language: en (English)...\n",
"Downloading http://nlp.stanford.edu/software/stanza/1.2.0/en/default.zip: 100%|██████████| 411M/411M [01:14<00:00, 5.48MB/s]\n",
"2021-05-31 21:36:11 INFO: Finished downloading models and saved to /root/stanza_resources.\n",
"Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/master/resources_1.2.0.json: 128kB [00:00, 21.2MB/s] \n",
"2021-05-31 21:36:11 INFO: Downloading default packages for language: es (Spanish)...\n",
"Downloading http://nlp.stanford.edu/software/stanza/1.2.0/es/default.zip: 100%|██████████| 566M/566M [01:43<00:00, 5.47MB/s]\n",
"2021-05-31 21:38:03 INFO: Finished downloading models and saved to /root/stanza_resources.\n",
"2021-05-31 21:38:03 INFO: Loading these models for language: en (English):\n",
"=========================\n",
"| Processor | Package |\n",
"-------------------------\n",
"| tokenize | combined |\n",
"| pos | combined |\n",
"| lemma | combined |\n",
"| depparse | combined |\n",
"| sentiment | sstplus |\n",
"| ner | ontonotes |\n",
"=========================\n",
"\n",
"2021-05-31 21:38:03 INFO: Use device: cpu\n",
"2021-05-31 21:38:03 INFO: Loading: tokenize\n",
"2021-05-31 21:38:03 INFO: Loading: pos\n",
"2021-05-31 21:38:04 INFO: Loading: lemma\n",
"2021-05-31 21:38:04 INFO: Loading: depparse\n",
"2021-05-31 21:38:04 INFO: Loading: sentiment\n",
"2021-05-31 21:38:05 INFO: Loading: ner\n",
"2021-05-31 21:38:05 INFO: Done loading processors!\n",
"2021-05-31 21:38:05 INFO: Loading these models for language: es (Spanish):\n",
"=======================\n",
"| Processor | Package |\n",
"-----------------------\n",
"| tokenize | ancora |\n",
"| mwt | ancora |\n",
"| pos | ancora |\n",
"| lemma | ancora |\n",
"| depparse | ancora |\n",
"| ner | conll02 |\n",
"=======================\n",
"\n",
"2021-05-31 21:38:05 INFO: Use device: cpu\n",
"2021-05-31 21:38:05 INFO: Loading: tokenize\n",
"2021-05-31 21:38:05 INFO: Loading: mwt\n",
"2021-05-31 21:38:05 INFO: Loading: pos\n",
"2021-05-31 21:38:06 INFO: Loading: lemma\n",
"2021-05-31 21:38:06 INFO: Loading: depparse\n",
"2021-05-31 21:38:06 INFO: Loading: ner\n",
"2021-05-31 21:38:08 INFO: Done loading processors!\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cU_atWZnmfMK"
},
"source": [
"### Import NLTK dependencies"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0I0h91rAmiLx",
"outputId": "60dd22c5-738a-4749-8a86-e191722eec19"
},
"source": [
"import nltk\n",
"\n",
"from nltk.corpus import stopwords\n",
"from nltk.sentiment import SentimentIntensityAnalyzer\n",
"from nltk.corpus import cess_esp as cess\n",
"from nltk import UnigramTagger as ut\n",
"from nltk import DefaultTagger as dt\n",
"\n",
"nltk.download('stopwords')\n",
"english_stopwords = stopwords.words(\"english\")\n",
"spanish_stopwords = stopwords.words(\"spanish\")\n",
"\n",
"nltk.download('vader_lexicon')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
" warnings.warn(\"The twython library has not been installed. \"\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n",
"[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ma87NX2Jm5Bs"
},
"source": [
"### Import Numpy, Pandas & sklearn"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bA21XPPdmqBf"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.feature_extraction.text import TfidfTransformer\n",
"from sklearn_pandas import DataFrameMapper\n",
"from sklearn.metrics import precision_recall_fscore_support\n",
"from sklearn.metrics import classification_report\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"from sklearn.model_selection import train_test_split"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "a2a9haVTnO94"
},
"source": [
"### Import multiple dependencies"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JD5R9fgDnRcP",
"outputId": "c025a56c-da83-4997-abb7-9e3cd884ec0d"
},
"source": [
"from google.colab import drive\n",
"\n",
"!pip install tweet-preprocessor\n",
"import preprocessor as p\n",
"\n",
"!pip install textblob\n",
"from textblob import TextBlob, Word\n",
"\n",
"!pip install texthero\n",
"import texthero as hero\n",
"\n",
"!pip install spanish_sentiment_analysis\n",
"from classifier import *\n",
"\n",
"from string import ascii_letters\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting tweet-preprocessor\n",
" Downloading https://files.pythonhosted.org/packages/17/9d/71bd016a9edcef8860c607e531f30bd09b13103c7951ae73dd2bf174163c/tweet_preprocessor-0.6.0-py3-none-any.whl\n",
"Installing collected packages: tweet-preprocessor\n",
"Successfully installed tweet-preprocessor-0.6.0\n",
"Requirement already satisfied: textblob in /usr/local/lib/python3.7/dist-packages (0.15.3)\n",
"Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.7/dist-packages (from textblob) (3.2.5)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->textblob) (1.15.0)\n",
"Collecting texthero\n",
" Downloading https://files.pythonhosted.org/packages/1f/5a/a9d33b799fe53011de79d140ad6d86c440a2da1ae8a7b24e851ee2f8bde8/texthero-1.0.9-py3-none-any.whl\n",
"Requirement already satisfied: matplotlib>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from texthero) (3.2.2)\n",
"Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from texthero) (0.22.2.post1)\n",
"Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from texthero) (3.0.6)\n",
"Requirement already satisfied: wordcloud>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from texthero) (1.5.0)\n",
"Collecting nltk>=3.3\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/5e/37/9532ddd4b1bbb619333d5708aaad9bf1742f051a664c3c6fa6632a105fd8/nltk-3.6.2-py3-none-any.whl (1.5MB)\n",
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"Collecting unidecode>=1.1.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/9e/25/723487ca2a52ebcee88a34d7d1f5a4b80b793f179ee0f62d5371938dfa01/Unidecode-1.2.0-py2.py3-none-any.whl (241kB)\n",
"\u001b[K |████████████████████████████████| 245kB 14.2MB/s \n",
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"Installing collected packages: nltk, unidecode, texthero\n",
" Found existing installation: nltk 3.2.5\n",
" Uninstalling nltk-3.2.5:\n",
" Successfully uninstalled nltk-3.2.5\n",
"Successfully installed nltk-3.6.2 texthero-1.0.9 unidecode-1.2.0\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"nltk"
]
}
}
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/spacy/util.py:717: UserWarning: [W094] Model 'en_core_web_sm' (2.2.5) specifies an under-constrained spaCy version requirement: >=2.2.2. This can lead to compatibility problems with older versions, or as new spaCy versions are released, because the model may say it's compatible when it's not. Consider changing the \"spacy_version\" in your meta.json to a version range, with a lower and upper pin. For example: >=3.0.6,<3.1.0\n",
" warnings.warn(warn_msg)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
"You can now load the package via spacy.load('en_core_web_sm')\n",
"Collecting spanish_sentiment_analysis\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/9f/7b/f9864d17910f3b6c9ed7b3a7cda279a76dcf9bc92a9282536d90dc789650/spanish_sentiment_analysis-1.0.0-py3-none-any.whl (15.8MB)\n",
"\u001b[K |████████████████████████████████| 15.8MB 435kB/s \n",
"\u001b[?25hRequirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (from spanish_sentiment_analysis) (0.0)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from spanish_sentiment_analysis) (1.4.1)\n",
"Collecting marisa-trie\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/20/95/d23071d0992dabcb61c948fb118a90683193befc88c23e745b050a29e7db/marisa-trie-0.7.5.tar.gz (270kB)\n",
"\u001b[K |████████████████████████████████| 276kB 51.2MB/s \n",
"\u001b[?25hRequirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from spanish_sentiment_analysis) (3.6.2)\n",
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"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sklearn->spanish_sentiment_analysis) (0.22.2.post1)\n",
"Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from nltk->spanish_sentiment_analysis) (2019.12.20)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk->spanish_sentiment_analysis) (7.1.2)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk->spanish_sentiment_analysis) (4.41.1)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk->spanish_sentiment_analysis) (1.0.1)\n",
"Building wheels for collected packages: marisa-trie\n",
" Building wheel for marisa-trie (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for marisa-trie: filename=marisa_trie-0.7.5-cp37-cp37m-linux_x86_64.whl size=860700 sha256=3ac4ecf03dbf80d952f676500574beb7379084c666510a8bfe5e5013b2be5cc2\n",
" Stored in directory: /root/.cache/pip/wheels/45/24/79/022624fc914f0e559fe8a1141aaff1f9df810905a13fc75d57\n",
"Successfully built marisa-trie\n",
"Installing collected packages: marisa-trie, spanish-sentiment-analysis\n",
"Successfully installed marisa-trie-0.7.5 spanish-sentiment-analysis-1.0.0\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning:\n",
"\n",
"sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
"\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Will download some functions from the nltk package if not found on the computer\n",
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "la3qd8f8nhT8"
},
"source": [
"### Load data"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uVGMZ38snjWs",
"outputId": "39aeb588-3f52-4d8c-839f-d7ccc97fb84a"
},
"source": [
"drive.mount('/content/gdrive')\n",
"df_test = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/data/EXIST2021_test_labeled.tsv\", sep=\"\\t\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/gdrive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BfKPCbZpnx8B"
},
"source": [
"### POS tagging, extract hashtags, count mentions and hashtags & calculate sentiment"
]
},
{
"cell_type": "code",
"metadata": {
"id": "K7tkg_mSn0XY"
},
"source": [
"p.set_options(p.OPT.URL, p.OPT.MENTION, p.OPT.HASHTAG, p.OPT.RESERVED, p.OPT.EMOJI, p.OPT.SMILEY, p.OPT.NUMBER)\n",
"\n",
"sentiment_clf_EN = SentimentIntensityAnalyzer()\n",
"sentiment_clf_ES = SentimentClassifier()\n",
"\n",
"\n",
"def pos_tagging(row):\n",
" if (row['language'] == 'en'):\n",
" doc = stanza_nlp_EN(row['text'])\n",
" else:\n",
" doc = stanza_nlp_ES(row['text'])\n",
"\n",
" return \" \".join([word.xpos for sent in doc.sentences for word in sent.words])\n",
"\n",
"\n",
"def extract_hashtags(row):\n",
" return ' '.join([word for word in re.findall(r\"#(\\w+)\", row['text'])])\n",
"\n",
"\n",
"def count_mentions(row):\n",
" found = re.findall('MENTION', p.tokenize(row['text']))\n",
"\n",
" total = 0\n",
" for i in found:\n",
" total += 1\n",
"\n",
" return total\n",
"\n",
"\n",
"def count_hashtags(row):\n",
" found = re.findall('HASHTAG', p.tokenize(row['text']))\n",
"\n",
" total = 0\n",
" for i in found:\n",
" total += 1\n",
"\n",
" return total\n",
"\n",
"\n",
"def calc_sentiment(row):\n",
" if(row['language'] == 'en'):\n",
" # sentiment normalized in range [0, 1]\n",
" return (sentiment_clf_EN.polarity_scores(row['text'])['compound'] - (-1)) / (1 - (-1))\n",
" else:\n",
" return sentiment_clf_ES.predict(row['text'])\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sLJqCFksoapZ"
},
"source": [
"df_test['pos'] = df_test.apply(pos_tagging, axis=1)\n",
"df_test['hashtags'] = df_test.apply(extract_hashtags, axis=1)\n",
"df_test['mention_count'] = df_test.apply(count_mentions, axis=1)\n",
"df_test['hashtag_count'] = df_test.apply(count_hashtags, axis=1)\n",
"df_test['sentiment'] = df_test.apply(calc_sentiment, axis=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "_80CscKStNjd"
},
"source": [
"### Preprocess text"
]
},
{
"cell_type": "code",
"metadata": {
"id": "dA5re48dqqYC"
},
"source": [
"stopword_en = nltk.corpus.stopwords.words('english')\n",
"stopword_es = nltk.corpus.stopwords.words('spanish')\n",
"\n",
"\n",
"def remove_diacritics(row):\n",
" return hero.remove_diacritics(pd.Series(row['text'])).values[0]\n",
"\n",
"\n",
"def basic_preprocess(row):\n",
" text = row['text'].lower()\n",
" text = hero.remove_digits(pd.Series(text)).values[0]\n",
" text = hero.remove_brackets(pd.Series(text)).values[0]\n",
" text = hero.remove_punctuation(pd.Series(text)).values[0]\n",
" text = hero.remove_whitespace(pd.Series(text)).values[0]\n",
" return text\n",
"\n",
"\n",
"def clean_tweet(row):\n",
" text = row['text']\n",
" text = p.clean(text)\n",
" return text\n",
"\n",
"\n",
"def remove_stopwords(row):\n",
" if(row['language'] == 'en'):\n",
" text = ' '.join([word for word in row['text'].split(' ') if not word in stopword_en])\n",
" return text\n",
" else:\n",
" text = ' '.join([word for word in row['text'].split(' ') if not word in stopword_es])\n",
" return text"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PBrPCWN2tZ3i"
},
"source": [
"df_test['text'] = df_test.apply(remove_diacritics, axis=1)\n",
"df_test['text'] = df_test.apply(clean_tweet, axis=1)\n",
"df_test['text'] = df_test.apply(basic_preprocess, axis=1)\n",
"df_test['text'] = df_test.apply(remove_stopwords, axis=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "310h7l0XkJ-e"
},
"source": [
"savepoint = df_test"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "FEgG6ncWtiSR"
},
"source": [
"### Lemmatization"
]
},
{
"cell_type": "code",
"metadata": {
"id": "lhAf2Hpitgye"
},
"source": [
"def lemmatization(row):\n",
" if (row['language'] == 'en'):\n",
" doc = stanza_nlp_EN(row['text'])\n",
" else:\n",
" doc = stanza_nlp_ES(row['text'])\n",
"\n",
" return \" \".join([word.lemma for sent in doc.sentences for word in sent.words])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zQzSze-WtuAW",
"outputId": "6f7441b5-de0f-4810-b8cb-377362186c23",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 172
}
},
"source": [
"df_test['text'] = df_test.apply(lemmatization, axis=1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-d59dc43aee05>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlemmatization\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'df_test' is not defined"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kTQUJnzNt9wb"
},
"source": [
"### Extract bias features"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ifF6yDV0t_JY"
},
"source": [
"# List of assertive verbs extracted from:\n",
"# Joan B. Hooper. 1975. On assertive predicates. In J. Kimball, editor,\n",
"# Syntax and Semantics, volume 4, pages 91–124. Academic Press, New York.\n",
"assertive_verbs_EN = ['think', 'believe', 'suppose', 'expect', 'imagine', 'guess', 'seem', 'appear', 'figure', 'acknowledge', 'admit', 'affirm', 'allege', 'answer', 'argue', 'assert', 'assure', 'certify', 'charge', 'claim', 'contend', 'declare', 'divulge', 'emphasize', 'explain', 'grant', 'guarantee', 'hint', 'hypothesize', 'imply', 'indicate', 'insist', 'intimate', 'maintain', 'mention', 'point out', 'predict', 'prophesy', 'postulate', 'remark', 'reply', 'report', 'say', 'state', 'suggest', 'swear', 'testify', 'theorize', 'verify', 'vow', 'write', 'agree', 'afraid', 'certain', 'sure', 'clear', 'obvious', 'evident', 'calculate', 'decide', 'deduce', 'estimate', 'hope', 'presume', 'surmise', 'suspect']\n",
"assertive_verbs_ES = ['pensar', 'creer', 'suponer', 'suponer', 'imaginar', 'adivinar', 'parecer', 'aparecer', 'figurar', 'reconocer', 'admitir', 'afirmar', 'alegar', 'responder', 'discutir', 'afirmar', 'asegurar', 'certificar', 'cargar', 'afirmar', 'contender', 'declarar', 'divulgar', 'enfatizar', 'explicar', 'conceder', 'garantizar', 'pista', 'hipotetizar', 'implicar', 'indicar', 'insistir', 'intimar', 'mantener', 'mencionar', 'señalar', 'predecir', 'profetizar', 'postular', 'observación', 'respuesta', 'informe', 'decir', 'expresar', 'sugerir', 'jurar', 'testificar', 'teorizar', 'verificar', 'votar', 'escribir', 'estar de acuerdo', 'asustado', 'estar seguro', 'obviar', 'evidenciar', 'calcular', 'decidir', 'deducir', 'estimar', 'tener esperanza', 'presumir', 'conjetura', 'sospechar ']\n",
"assertive_verbs_EN_preprocessed = []\n",
"assertive_verbs_ES_preprocessed = []\n",
"\n",
"for word in assertive_verbs_EN:\n",
" doc = stanza_nlp_EN(word)\n",
" assertive_verbs_EN_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"for word in assertive_verbs_ES:\n",
" doc = stanza_nlp_ES(word)\n",
" assertive_verbs_ES_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"\n",
"def count_assertive_verbs(row):\n",
" count = 0\n",
" if(row['language'] == 'en'):\n",
" for word in assertive_verbs_EN_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
" else:\n",
" for word in assertive_verbs_ES_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
"\n",
"\n",
"# List of factive verbs extracted from:\n",
"# Joan B. Hooper. 1975. On assertive predicates. In J. Kimball, editor,\n",
"# Syntax and Semantics, volume 4, pages 91–124. Academic Press, New York.\n",
"factive_verbs_EN = ['know', 'realize', 'regret', 'forget', 'find out', 'discover', 'learn', 'note', 'notice', 'observe', 'perceive', 'recall', 'remember', 'reveal', 'see', 'resent', 'amuse', 'suffice', 'bother', 'make sense', 'care', 'odd', 'strange', 'interesting', 'relevant', 'sorry', 'exciting']\n",
"factive_verbs_ES = ['saber', 'darse cuenta', 'arrepentirse', 'olvidar', 'descubrir', 'aprender', 'notar', 'avisar', 'observar', 'percibir', 'recuerdo', 'recordar', 'revelar', 'ver', 'resentir', 'entretener', 'satisfacer', 'molestar', 'tener sentido', 'cuidadar', 'extrañar', 'interesar', 'ser relevante', 'sentir', 'emocionar']\n",
"factive_verbs_EN_preprocessed = []\n",
"factive_verbs_ES_preprocessed = []\n",
"for word in factive_verbs_EN:\n",
" doc = stanza_nlp_EN(word)\n",
" factive_verbs_EN_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"for word in factive_verbs_ES:\n",
" doc = stanza_nlp_ES(word)\n",
" factive_verbs_ES_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"\n",
"def count_factive_verbs(row):\n",
" count = 0\n",
" if(row['language'] == 'en'):\n",
" for word in factive_verbs_EN_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
" else:\n",
" for word in factive_verbs_ES_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
"\n",
"\n",
"# List of implicative verbs extracted from:\n",
"# Lauri Karttunen. 1971. Implicative verbs. Language, 47(2):340–358.\n",
"implicative_verbs_EN = ['manage', 'remember', 'bother', 'get', 'dare', 'care', 'venture', 'condescend', 'happen', 'fit', 'careful', 'misfortune', 'sense', 'succeed', 'deign', 'forget', 'fail', 'neglect', 'decline', 'avoid', 'refrain', 'choose', 'able', 'can', 'cause', 'force', 'prevent', 'preclude', 'keep', 'allow', 'hesitate', 'attempt']\n",
"implicative_verbs_ES = ['administrar', 'recordar', 'molestar', 'obtener', 'atreverse', 'cuidar', 'condescender', 'suceder', 'encajar', 'tener cuidado', 'desgraciar', 'sentir', 'triunfar', 'dignarse', 'olvidar', 'fallar', 'negligencia', 'disminuir', 'evitar', 'escoger', 'poder', 'causar', 'forzar', 'evitar', 'imposibilitar', 'mantener', 'permitir', 'dudar', 'intentar']\n",
"implicative_verbs_EN_preprocessed = []\n",
"implicative_verbs_ES_preprocessed = []\n",
"for word in implicative_verbs_EN:\n",
" doc = stanza_nlp_EN(word)\n",
" implicative_verbs_EN_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"for word in implicative_verbs_ES:\n",
" doc = stanza_nlp_ES(word)\n",
" implicative_verbs_ES_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"\n",
"def count_implicative_verbs(row):\n",
" count = 0\n",
" if(row['language'] == 'en'):\n",
" for word in implicative_verbs_EN_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
" else:\n",
" for word in implicative_verbs_ES_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
"\n",
"\n",
"# List of hedges extracted from:\n",
"# Ken Hyland. 2005. Metadiscourse: Exploring Interaction in Writing.\n",
"# Continuum, London and New York.\n",
"hedge_words_EN = ['about', 'almost', 'apparent', 'apparently', 'appear', 'appeared', 'appears', 'approximately', 'around', 'assume', 'assumed', 'certain amount', 'certain extent', 'certain level', 'claim', 'claimed', 'could', \"couldn't\", 'doubt', 'doubtful', 'essentially', 'estimate', 'estimated', 'feel', 'felt', 'frequently', 'from our perspective', 'generally', 'guess', 'in general', 'in most cases', 'in most instances', 'in our view', 'indicate', 'indicated', 'largely', 'likely', 'mainly', 'may', 'maybe', 'might', 'mostly', 'often', 'on the whole', 'ought', 'perhaps', 'plausible', 'plausibly', 'possible', 'possibly', 'postulate', 'postulated', 'presumable', 'probable', 'probably', 'relatively', 'roughly', 'seems', 'should', 'sometimes', 'somewhat', 'suggest', 'suggested', 'suppose', 'suspect', 'tend to', 'tends to', 'typical', 'typically', 'uncertain', 'uncertainly', 'unclear', 'unclearly', 'unlikely', 'usually', 'would', 'broadly', 'tended to', 'presumably', 'suggests', 'from this perspective', 'from my perspective', 'in my view', 'in this view', 'in our opinion', 'in my opinion', 'to my knowledge', 'fairly', 'quite', 'rather', 'argue', 'argues', 'argued', 'claims', 'feels', 'indicates', 'supposed', 'supposes', 'suspects', 'postulates']\n",
"hedge_words_ES = ['acerca de', 'casi', 'aparente', 'aparentemente', 'aparecer', 'apareció', 'aparece', 'aproximadamente', 'alrededor', 'asumir', 'ficticio', 'cierta cantidad', 'cierto punto', 'cierto nivel', 'afirmar', 'reclamado', 'pudo', 'no podría', 'duda', 'dudoso', 'esencialmente', 'estimar', 'estimado', 'sentir', 'sintió', 'frecuentemente', 'desde nuestra perspectiva', 'generalmente', 'adivinar', 'en general', 'en la mayoria de los casos', 'en nuestra opinión', 'indicar', 'indicado', 'en gran parte', 'probable', 'principalmente', 'mayo', 'quizás', 'podría', 'principalmente', 'a menudo', 'en conjunto', 'debería', 'quizás', 'plausible', 'plausiblemente', 'posible', 'posiblemente', 'postulado', 'probable', 'probablemente', 'relativamente', 'aproximadamente', 'parece', 'deberían', 'algunas veces', 'un poco', 'sugerir', 'sugirió', 'suponer', 'sospechar', 'tiende a', 'típico', 'típicamente', 'incierto', 'inciertamente', 'poco claro', 'improbable', 'por lo general', 'haría', 'en general', 'presumiblemente', 'sugiere', 'desde esta perspectiva', 'desde mi perspectiva', 'en mi vista', 'en esta vista', 'en nuestra opinion', 'en mi opinión', 'que yo sepa', 'equitativamente', 'bastante', 'discutir', 'argumenta', 'reclamación', 'siente', 'indica', 'supuesto', 'supone', 'sospechoso', 'postulado']\n",
"hedge_words_EN_preprocessed = []\n",
"hedge_words_ES_preprocessed = []\n",
"for word in hedge_words_EN:\n",
" doc = stanza_nlp_EN(word)\n",
" hedge_words_EN_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"for word in hedge_words_ES:\n",
" doc = stanza_nlp_ES(word)\n",
" hedge_words_ES_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"\n",
"def count_hedge_words(row):\n",
" count = 0\n",
" if(row['language'] == 'en'):\n",
" for word in hedge_words_EN_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
" else:\n",
" for word in hedge_words_ES_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
"\n",
"\n",
"strong_subjective_words_EN = ['tyranny', 'scum', 'smokecreen', 'bully', 'apologist', 'devil', 'barbarian', 'liar', 'belligerence', 'pariah', 'condemnation', 'venom', 'sactimonious', 'diatribe', 'exaggeration', 'mockery', 'repudiation', 'anguish', 'insinuation', 'fallacies', 'antagonism', 'evil', 'atrocities', 'genius', 'denunciation', 'goodwill', 'exploitation', 'injustice', 'humuliation', 'innuendo', 'ill-treatment', 'revenge', 'sympathy', 'rogue']\n",
"strong_subjective_words_ES = ['tiranía', 'escoria', 'cortina de humo', 'matón', 'apologista', 'diablo', 'bárbaro', 'mentiroso', 'beligerancia', 'paria', 'condenación', 'veneno' , 'santurrón', 'diatriba', 'exageración', 'burla', 'repudio', 'angustia', 'insinuación', 'falacias', 'antagonismo', 'maldad', 'atrocidades', 'genio', 'denuncia', 'buena voluntad', 'explotación', 'injusticia', 'humillación', 'insinuación', 'malos tratos', 'venganza', 'simpatía', 'pícaro']\n",
"strong_subjective_words_EN_preprocessed = []\n",
"strong_subjective_words_ES_preprocessed = []\n",
"for word in strong_subjective_words_EN:\n",
" doc = stanza_nlp_EN(word)\n",
" strong_subjective_words_EN_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"for word in strong_subjective_words_ES:\n",
" doc = stanza_nlp_ES(word)\n",
" strong_subjective_words_ES_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"\n",
"def count_strong_subjective_words(row):\n",
" count = 0\n",
" if(row['language'] == 'en'):\n",
" for word in strong_subjective_words_EN_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
" else:\n",
" for word in strong_subjective_words_ES_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
"\n",
"weak_subjective_words_EN = ['aberration', 'plague', 'allusion', 'risk', 'apprenhensions', 'drama', 'beneficiary', 'trick', 'resistant', 'promise', 'credence', 'intrigue', 'distortion', 'unity', 'eyebrows', 'failures', 'inclination', 'tolerance', 'liability', 'persistent', 'assault', 'trust', 'benefit', 'success', 'blood', 'spirit', 'controversy', 'slump', 'likelihood', 'sincerity', 'peaceful', 'eternity', 'pressure', 'rejection']\n",
"weak_subjective_words_ES = ['aberración', 'plaga', 'alusión', 'riesgo', 'aprensiones', 'drama', 'beneficiario', 'truco', 'resistente', 'promesa', 'credibilidad', 'intriga' , 'distorsión', 'unidad', 'cejas', 'fracasos', 'inclinación', 'tolerancia', 'responsabilidad', 'persistente', 'asalto', 'confianza', 'beneficio', 'éxito', 'sangre', 'espíritu', 'controversia', 'depresión', 'probabilidad', 'sinceridad', 'pacífica', 'eternidad', 'presión', 'rechazo']\n",
"weak_subjective_words_EN_preprocessed = []\n",
"weak_subjective_words_ES_preprocessed = []\n",
"for word in weak_subjective_words_EN:\n",
" doc = stanza_nlp_EN(word)\n",
" weak_subjective_words_EN_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"for word in weak_subjective_words_ES:\n",
" doc = stanza_nlp_ES(word)\n",
" weak_subjective_words_ES_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"\n",
"def count_weak_subjective_words(row):\n",
" count = 0\n",
" if(row['language'] == 'en'):\n",
" for word in weak_subjective_words_EN_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
" else:\n",
" for word in weak_subjective_words_ES_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
"\n",
"biased_words_EN = [\"slut\", \"whore\", \"bitch\", \"floozy\", \"tramp\", \"slutty\", \"bimbo\", \"fagot\", \"douchebag\", \"prude\", \"asshole\", \"bitchy\", \"twit\", \"gold digger\", \"groupie\", \"pussy\", \"horny\", \"drag queen\", \"vixen\", \"blonde\", \"scumbag\", \"bastard\", \"moron\", \"liar\", \"hypocrite\", \"jackass\", \"douche\", \"fucking\", \"ditzy\", \"gal\", \"sleazy\", \"womanizer\", \"virginal\", \"trashy\", \"comely sassy\", \"lecherous\", \"vagrant\", \"hobo\", \"drunkard\", \"chap slob\", \"wanderer\", \"bloke\", \"drifter\", \"boozer\", \"penniless\" ]\n",
"biased_words_ES = ['termino', 'alcahueta', 'alemanita', 'amante', 'amargada', 'anabolena', 'arpía', 'asquerosa', 'avariuda', 'bagasa', 'barragana', 'barriobajera', 'bataclana', 'bordiona', 'burraca', 'burracona', 'buscona', 'bruja', 'cabaretera', 'cabrona', 'calentorra', 'calientacamas', 'calientahuevos', 'calientapollas', 'cerda', 'cambri', 'candonga', 'cantonera', 'casquivana', 'celestina', 'celosa', 'chai', 'chango', 'chingada', 'chirlata', 'choni', 'choriza', 'chumascona', 'chuminista', 'chupapijas', 'chupetera', 'churrera', 'cisne', 'cleopatra', 'cocota', 'cocu', 'coima', 'colipoterra', 'concha', 'conchita', 'concubina', 'coneja', 'cortesana', 'costillera', 'cualquiera', 'currulaca', 'daifa', 'descarriada', 'deshonesta', 'disoluta', 'diva', 'elementa', 'empaná', 'enrayada', 'envidiosa', 'espatarrada', 'esquinera', 'facilona', 'falsa', 'fea', 'feminazi', 'folladora', 'fresca', 'fuellera', 'fulana', 'furcia', 'golfa', 'guarra', 'gueisa', 'gumia', 'hetaira', 'huevera', 'hurgamandera', 'hurona', 'iza', 'jabata', 'lacroilla', 'ladillera', 'lagarta', 'lagartera', 'lagartona', 'lea', 'leona', 'leonesa', 'libertina', 'ligerita', 'loca', 'lumi', 'lumia', 'lumiasca', 'madam', 'mala', 'malparida', 'malparia', 'mamadera', 'mamona', 'manceba', 'manfla', 'manipuladora', 'mentirosa', 'meretriz', 'mesalina', 'microondas', 'mina', 'moma', 'mona', 'mortadela', 'mozcorra', 'mujerzuela', 'niñata', 'nocturna', 'ordeñadora', 'pájara', 'pajarera', 'pajarona', 'pajillera', 'pantera', 'patín', 'patinadora', 'pelandusca', 'pendeja', 'pendón', 'perendeca', 'perica', 'perra', 'pesetera', 'piculina', 'pilingui', 'pingona', 'polilla', 'pollera', 'puerca', 'pringá', 'pringa', 'pringada', 'prosti', 'prostituta', 'pujicama', 'pupila', 'puta', 'quedona', 'ramera', 'rastrera', 'rebuscona', 'rufa', 'sabanera', 'soldadera', 'sota', 'subidita', 'sumisa', 'suripanta', 'taconera', 'tierrosa', 'tigresa', 'tipa', 'tiparraca', 'tortillera', 'trabuquera', 'trotacalles', 'trotadora', 'trotera', 'trotona', 'trufera', 'turquesa', 'verdulera', 'vieja', 'vulgar', 'zorra', 'zurriaga', 'terruca', 'cochina', 'ladrona', 'ratera', 'traicionera']\n",
"biased_words_EN_preprocessed = []\n",
"biased_words_ES_preprocessed = []\n",
"for word in biased_words_EN:\n",
" doc = stanza_nlp_EN(word)\n",
" biased_words_EN_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"for word in biased_words_ES:\n",
" doc = stanza_nlp_ES(word)\n",
" biased_words_ES_preprocessed.append(\" \".join([word.lemma for sent in doc.sentences for word in sent.words]))\n",
"\n",
"def count_biased_words(row):\n",
" count = 0\n",
" if(row['language'] == 'en'):\n",
" for word in biased_words_EN_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
" else:\n",
" for word in biased_words_ES_preprocessed:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
"\n",
"\n",
"def compute_distance_between_biased_words(row):\n",
" if (row['biased_words'] == 0):\n",
" return 0\n",
"\n",
" elif (row['biased_words'] == 1):\n",
" return 1\n",
"\n",
" else:\n",
" text = row['text']\n",
" selected_indices = []\n",
" for i in range (0, len(text.split(\" \"))):\n",
" if text.split(\" \")[i] in biased_words_ES_preprocessed:\n",
" selected_indices.append(i)\n",
"\n",
" result = 0\n",
" for i in range(0, len(selected_indices) - 1):\n",
" result = result + (selected_indices[i+1] - selected_indices[i])\n",
"\n",
" return result / row['biased_words']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LfwMs1VyuEXZ"
},
"source": [
"df_test['assertive_verbs'] = df_test.apply(count_assertive_verbs, axis=1)\n",
"df_test['factive_verbs'] = df_test.apply(count_factive_verbs, axis=1)\n",
"df_test['hedge_words'] = df_test.apply(count_hedge_words, axis=1)\n",
"df_test['implicative_verbs'] = df_test.apply(count_implicative_verbs, axis=1)\n",
"df_test['strong_subjective_words'] = df_test.apply(count_strong_subjective_words, axis=1)\n",
"df_test['weak_subjective_words'] = df_test.apply(count_weak_subjective_words, axis=1)\n",
"df_test['biased_words'] = df_test.apply(count_biased_words, axis=1)\n",
"df_test['distance_biased_words'] = df_test.apply(compute_distance_between_biased_words, axis=1)\n",
"\n",
"df_test['hedge_words'][df_test['hedge_words'] > 0] = 1\n",
"df_test['assertive_verbs'][df_test['assertive_verbs'] > 0] = 1\n",
"df_test['factive_verbs'][df_test['factive_verbs'] > 0] = 1\n",
"df_test['implicative_verbs'][df_test['implicative_verbs'] > 0] = 1\n",
"df_test['strong_subjective_words'][df_test['strong_subjective_words'] > 0] = 1\n",
"df_test['weak_subjective_words'][df_test['weak_subjective_words'] > 0] = 1"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fMrQ5Tk4Qvea"
},
"source": [
"### Extract hurtlex features"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-cqQbjhsQ5qP"
},
"source": [
"hurtlex_en = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/data/hurtlex_EN.tsv\", sep=\"\\t\")\n",
"hurtlex_es = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/data/hurtlex_ES.tsv\", sep=\"\\t\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "muhvuPBcQ6sO"
},
"source": [
"PS_words_EN = hurtlex_en[hurtlex_en['category'] == 'ps']['lemma'].values.tolist() \t # negative stereotypes ethnic slurs\n",
"RCI_words_EN = hurtlex_en[hurtlex_en['category'] == 'rci']['lemma'].values.tolist() \t # locations and demonyms\n",
"PA_words_EN = hurtlex_en[hurtlex_en['category'] == 'pa']['lemma'].values.tolist() \t # professions and occupations\n",
"DDF_words_EN = hurtlex_en[hurtlex_en['category'] == 'ddf']['lemma'].values.tolist() \t # physical disabilities and diversity\n",
"DDP_words_EN = hurtlex_en[hurtlex_en['category'] == 'ddp']['lemma'].values.tolist() \t # cognitive disabilities and diversity\n",
"DMC_words_EN = hurtlex_en[hurtlex_en['category'] == 'dmc']['lemma'].values.tolist() \t # moral and behavioral defects\n",
"IS_words_EN = hurtlex_en[hurtlex_en['category'] == 'is']['lemma'].values.tolist() \t # words related to social and economic disadvantage\n",
"OR_words_EN = hurtlex_en[hurtlex_en['category'] == 'or']['lemma'].values.tolist() \t # plants\n",
"AN_words_EN = hurtlex_en[hurtlex_en['category'] == 'an']['lemma'].values.tolist() \t # animals\n",
"ASM_words_EN = hurtlex_en[hurtlex_en['category'] == 'asm']['lemma'].values.tolist() \t # male genitalia\n",
"ASF_words_EN = hurtlex_en[hurtlex_en['category'] == 'asf']['lemma'].values.tolist() \t # female genitalia\n",
"PR_words_EN = hurtlex_en[hurtlex_en['category'] == 'pr']['lemma'].values.tolist() \t # words related to prostitution\n",
"OM_words_EN = hurtlex_en[hurtlex_en['category'] == 'om']['lemma'].values.tolist() \t # words related to homosexuality\n",
"QAS_words_EN = hurtlex_en[hurtlex_en['category'] == 'qas']['lemma'].values.tolist() \t # with potential negative connotations\n",
"CDS_words_EN = hurtlex_en[hurtlex_en['category'] == 'cds']['lemma'].values.tolist() \t # derogatory words\n",
"RE_words_EN = hurtlex_en[hurtlex_en['category'] == 're']['lemma'].values.tolist() \t # felonies and words related to crime and immoral behavior\n",
"SVP_words_EN = hurtlex_en[hurtlex_en['category'] == 'svp']['lemma'].values.tolist() \t# words related to the seven deadly sins of the Christian tradition"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "iZscoSK-RCgk"
},
"source": [
"PS_words_ES = hurtlex_en[hurtlex_en['category'] == 'ps']['lemma'].values.tolist() \t # negative stereotypes ethnic slurs\n",
"RCI_words_ES = hurtlex_en[hurtlex_en['category'] == 'rci']['lemma'].values.tolist() \t # locations and demonyms\n",
"PA_words_ES = hurtlex_en[hurtlex_en['category'] == 'pa']['lemma'].values.tolist() \t # professions and occupations\n",
"DDF_words_ES = hurtlex_en[hurtlex_en['category'] == 'ddf']['lemma'].values.tolist() \t # physical disabilities and diversity\n",
"DDP_words_ES = hurtlex_en[hurtlex_en['category'] == 'ddp']['lemma'].values.tolist() \t # cognitive disabilities and diversity\n",
"DMC_words_ES = hurtlex_en[hurtlex_en['category'] == 'dmc']['lemma'].values.tolist() \t # moral and behavioral defects\n",
"IS_words_ES = hurtlex_en[hurtlex_en['category'] == 'is']['lemma'].values.tolist() \t # words related to social and economic disadvantage\n",
"OR_words_ES = hurtlex_en[hurtlex_en['category'] == 'or']['lemma'].values.tolist() \t # plants\n",
"AN_words_ES = hurtlex_en[hurtlex_en['category'] == 'an']['lemma'].values.tolist() \t # animals\n",
"ASM_words_ES = hurtlex_en[hurtlex_en['category'] == 'asm']['lemma'].values.tolist() \t # male genitalia\n",
"ASF_words_ES = hurtlex_en[hurtlex_en['category'] == 'asf']['lemma'].values.tolist() \t # female genitalia\n",
"PR_words_ES = hurtlex_en[hurtlex_en['category'] == 'pr']['lemma'].values.tolist() \t # words related to prostitution\n",
"OM_words_ES = hurtlex_en[hurtlex_en['category'] == 'om']['lemma'].values.tolist() \t # words related to homosexuality\n",
"QAS_words_ES = hurtlex_en[hurtlex_en['category'] == 'qas']['lemma'].values.tolist() \t # with potential negative connotations\n",
"CDS_words_ES = hurtlex_en[hurtlex_en['category'] == 'cds']['lemma'].values.tolist() \t # derogatory words\n",
"RE_words_ES = hurtlex_en[hurtlex_en['category'] == 're']['lemma'].values.tolist() \t # felonies and words related to crime and immoral behavior\n",
"SVP_words_ES = hurtlex_en[hurtlex_en['category'] == 'svp']['lemma'].values.tolist() \t# words related to the seven deadly sins of the Christian tradition"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FG8zwknEREvV"
},
"source": [
"def count_lexic_in_text(row, list_EN, list_ES):\n",
" count = 0\n",
" if(row['language'] == 'en'):\n",
" for word in list_EN:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
" else:\n",
" for word in list_ES:\n",
" count = count + row['text'].count(word)\n",
" return count\n",
"\n",
"df_test['ps_hurtlex'] = df_test.apply(count_lexic_in_text, args=(PS_words_EN, PS_words_ES), axis=1)\n",
"df_test['rci_hurtlex'] = df_test.apply(count_lexic_in_text, args=(RCI_words_EN, RCI_words_ES), axis=1)\n",
"df_test['pa_hurtlex'] = df_test.apply(count_lexic_in_text, args=(PA_words_EN, PA_words_ES), axis=1)\n",
"df_test['ddf_hurtlex'] = df_test.apply(count_lexic_in_text, args=(DDF_words_EN, DDF_words_ES), axis=1)\n",
"df_test['ddp_hurtlex'] = df_test.apply(count_lexic_in_text, args=(DDP_words_EN, DDP_words_ES), axis=1)\n",
"df_test['dmc_hurtlex'] = df_test.apply(count_lexic_in_text, args=(DMC_words_EN, DMC_words_ES), axis=1)\n",
"df_test['is_hurtlex'] = df_test.apply(count_lexic_in_text, args=(IS_words_EN, IS_words_ES), axis=1)\n",
"df_test['or_hurtlex'] = df_test.apply(count_lexic_in_text, args=(OR_words_EN, OR_words_ES), axis=1)\n",
"df_test['an_hurtlex'] = df_test.apply(count_lexic_in_text, args=(AN_words_EN, AN_words_ES), axis=1)\n",
"df_test['asm_hurtlex'] = df_test.apply(count_lexic_in_text, args=(ASM_words_EN, ASM_words_ES), axis=1)\n",
"df_test['asf_hurtlex'] = df_test.apply(count_lexic_in_text, args=(ASF_words_EN, ASF_words_ES), axis=1)\n",
"df_test['pr_hurtlex'] = df_test.apply(count_lexic_in_text, args=(PR_words_EN, PR_words_ES), axis=1)\n",
"df_test['om_hurtlex'] = df_test.apply(count_lexic_in_text, args=(OM_words_EN, OM_words_ES), axis=1)\n",
"df_test['qas_hurtlex'] = df_test.apply(count_lexic_in_text, args=(QAS_words_EN, QAS_words_ES), axis=1)\n",
"df_test['cds_hurtlex'] = df_test.apply(count_lexic_in_text, args=(CDS_words_EN, CDS_words_ES), axis=1)\n",
"df_test['re_hurtlex'] = df_test.apply(count_lexic_in_text, args=(RE_words_EN, RE_words_ES), axis=1)\n",
"df_test['svp_hurtlex'] = df_test.apply(count_lexic_in_text, args=(SVP_words_EN, SVP_words_ES), axis=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ldD-Kz9qe3_C"
},
"source": [
"### Prepare test dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "qAnshjk8e7Ht"
},
"source": [
"# df_test = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/data/EXIST2021_test.tsv\", sep=\"\\t\")\n",
"\n",
"df_test['pos'] = df_test.apply(pos_tagging, axis=1)\n",
"df_test['hashtags'] = df_test.apply(extract_hashtags, axis=1)\n",
"df_test['mention_count'] = df_test.apply(count_mentions, axis=1)\n",
"df_test['hashtag_count'] = df_test.apply(count_hashtags, axis=1)\n",
"df_test['sentiment'] = df_test.apply(calc_sentiment, axis=1)\n",
"\n",
"df_test['text'] = df_test.apply(remove_diacritics, axis=1)\n",
"df_test['text'] = df_test.apply(clean_tweet, axis=1)\n",
"df_test['text'] = df_test.apply(basic_preprocess, axis=1)\n",
"df_test['text'] = df_test.apply(remove_stopwords, axis=1)\n",
"\n",
"df_test['text'] = df_test.apply(lemmatization, axis=1)\n",
"\n",
"df_test['assertive_verbs'] = df_test.apply(count_assertive_verbs, axis=1)\n",
"df_test['factive_verbs'] = df_test.apply(count_factive_verbs, axis=1)\n",
"df_test['hedge_words'] = df_test.apply(count_hedge_words, axis=1)\n",
"df_test['implicative_verbs'] = df_test.apply(count_implicative_verbs, axis=1)\n",
"df_test['strong_subjective_words'] = df_test.apply(count_strong_subjective_words, axis=1)\n",
"df_test['weak_subjective_words'] = df_test.apply(count_weak_subjective_words, axis=1)\n",
"df_test['biased_words'] = df_test.apply(count_biased_words, axis=1)\n",
"df_test['distance_biased_words'] = df_test.apply(compute_distance_between_biased_words, axis=1)\n",
"\n",
"df_test['hedge_words'][df_test['hedge_words'] > 0] = 1\n",
"df_test['assertive_verbs'][df_test['assertive_verbs'] > 0] = 1\n",
"df_test['factive_verbs'][df_test['factive_verbs'] > 0] = 1\n",
"df_test['implicative_verbs'][df_test['implicative_verbs'] > 0] = 1\n",
"df_test['strong_subjective_words'][df_test['strong_subjective_words'] > 0] = 1\n",
"df_test['weak_subjective_words'][df_test['weak_subjective_words'] > 0] = 1\n",
"\n",
"df_test['ps_hurtlex'] = df_test.apply(count_lexic_in_text, args=(PS_words_EN, PS_words_ES), axis=1)\n",
"df_test['rci_hurtlex'] = df_test.apply(count_lexic_in_text, args=(RCI_words_EN, RCI_words_ES), axis=1)\n",
"df_test['pa_hurtlex'] = df_test.apply(count_lexic_in_text, args=(PA_words_EN, PA_words_ES), axis=1)\n",
"df_test['ddf_hurtlex'] = df_test.apply(count_lexic_in_text, args=(DDF_words_EN, DDF_words_ES), axis=1)\n",
"df_test['ddp_hurtlex'] = df_test.apply(count_lexic_in_text, args=(DDP_words_EN, DDP_words_ES), axis=1)\n",
"df_test['dmc_hurtlex'] = df_test.apply(count_lexic_in_text, args=(DMC_words_EN, DMC_words_ES), axis=1)\n",
"df_test['is_hurtlex'] = df_test.apply(count_lexic_in_text, args=(IS_words_EN, IS_words_ES), axis=1)\n",
"df_test['or_hurtlex'] = df_test.apply(count_lexic_in_text, args=(OR_words_EN, OR_words_ES), axis=1)\n",
"df_test['an_hurtlex'] = df_test.apply(count_lexic_in_text, args=(AN_words_EN, AN_words_ES), axis=1)\n",
"df_test['asm_hurtlex'] = df_test.apply(count_lexic_in_text, args=(ASM_words_EN, ASM_words_ES), axis=1)\n",
"df_test['asf_hurtlex'] = df_test.apply(count_lexic_in_text, args=(ASF_words_EN, ASF_words_ES), axis=1)\n",
"df_test['pr_hurtlex'] = df_test.apply(count_lexic_in_text, args=(PR_words_EN, PR_words_ES), axis=1)\n",
"df_test['om_hurtlex'] = df_test.apply(count_lexic_in_text, args=(OM_words_EN, OM_words_ES), axis=1)\n",
"df_test['qas_hurtlex'] = df_test.apply(count_lexic_in_text, args=(QAS_words_EN, QAS_words_ES), axis=1)\n",
"df_test['cds_hurtlex'] = df_test.apply(count_lexic_in_text, args=(CDS_words_EN, CDS_words_ES), axis=1)\n",
"df_test['re_hurtlex'] = df_test.apply(count_lexic_in_text, args=(RE_words_EN, RE_words_ES), axis=1)\n",
"df_test['svp_hurtlex'] = df_test.apply(count_lexic_in_text, args=(SVP_words_EN, SVP_words_ES), axis=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "EXNPUC8zRtdc"
},
"source": [
"### Correlation between independent and dependent variables"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hzWTn8ihSNn9"
},
"source": [
"new_df = df.copy()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Q9Ds1ZjMR1KJ"
},
"source": [
"df_features = ['task1', 'assertive_verbs', 'hedge_words', 'factive_verbs', 'implicative_verbs', 'strong_subjective_words', 'weak_subjective_words', 'biased_words', 'distance_biased_words', 'mention_count', 'hashtag_count', 'sentiment', 'ps_hurtlex', 'rci_hurtlex', 'pa_hurtlex', 'ddf_hurtlex', 'ddp_hurtlex', 'dmc_hurtlex', 'is_hurtlex', 'or_hurtlex', 'an_hurtlex', 'asm_hurtlex', 'asf_hurtlex', 'pr_hurtlex', 'om_hurtlex', 'qas_hurtlex', 'cds_hurtlex', 're_hurtlex', 'svp_hurtlex']\n",
"corr = new_df[df_features].apply(lambda x : pd.factorize(x)[0]).corr(method='pearson', min_periods=1)[['task1']].abs().sort_values(by='task1', ascending=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 612
},
"id": "ghcT4Ba-R58f",
"outputId": "8cf57148-8ea1-4096-b27a-538297789de2"
},
"source": [
"sns.set_theme(style=\"white\")\n",
"\n",
"# Generate a mask for the upper triangle\n",
"mask = np.triu(np.ones_like(corr, dtype=bool))\n",
"\n",
"# Set up the matplotlib figure\n",
"f, ax = plt.subplots(figsize=(3, 10))\n",
"\n",
"# Generate a custom diverging colormap\n",
"cmap = sns.diverging_palette(300, 21, as_cmap=True)\n",
"\n",
"# Draw the heatmap with the mask and correct aspect ratio\n",
"sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.25, center=0,\n",
" square=False, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f732b36e290>"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 216x720 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4rJvJeUFS2Gh"
},
"source": [
"def add_new_column_task2(row, c):\n",
" if(row['task2'] == c):\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"new_df['ideological-inequality'] = new_df.apply(add_new_column_task2, c='ideological-inequality', axis=1)\n",
"new_df['misogyny-non-sexual-violence'] = new_df.apply(add_new_column_task2, c='misogyny-non-sexual-violence', axis=1)\n",
"new_df['non-sexist'] = new_df.apply(add_new_column_task2, c='non-sexist', axis=1)\n",
"new_df['objectification'] = new_df.apply(add_new_column_task2, c='objectification', axis=1)\n",
"new_df['sexual-violence'] = new_df.apply(add_new_column_task2, c='sexual-violence', axis=1)\n",
"new_df['stereotyping-dominance'] = new_df.apply(add_new_column_task2, c='stereotyping-dominance', axis=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "13dpPbJhSGcn"
},
"source": [
"df_features = ['task2', 'ideological-inequality', 'misogyny-non-sexual-violence', 'objectification', 'stereotyping-dominance', 'sexual-violence', 'non-sexist', 'assertive_verbs', 'hedge_words', 'factive_verbs', 'implicative_verbs', 'strong_subjective_words', 'weak_subjective_words', 'biased_words', 'distance_biased_words', 'mention_count', 'hashtag_count', 'sentiment', 'ps_hurtlex', 'rci_hurtlex', 'pa_hurtlex', 'ddf_hurtlex', 'ddp_hurtlex', 'dmc_hurtlex', 'is_hurtlex', 'or_hurtlex', 'an_hurtlex', 'asm_hurtlex', 'asf_hurtlex', 'pr_hurtlex', 'om_hurtlex', 'qas_hurtlex', 'cds_hurtlex', 're_hurtlex', 'svp_hurtlex']\n",
"corr = new_df[df_features].apply(lambda x : pd.factorize(x)[0]).corr(method='pearson', min_periods=1)[['task2', 'ideological-inequality', 'misogyny-non-sexual-violence', 'objectification', 'stereotyping-dominance', 'sexual-violence', 'non-sexist']].abs().sort_values(by='task2', ascending=False)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 770
},
"id": "vzLgMOHWSYZE",
"outputId": "0be3a1d2-870d-4b39-d8c5-a6528a41f23f"
},
"source": [
"sns.set_theme(style=\"white\")\n",
"\n",
"# Generate a mask for the upper triangle\n",
"mask = np.triu(np.ones_like(corr, dtype=bool))\n",
"\n",
"# Set up the matplotlib figure\n",
"f, ax = plt.subplots(figsize=(10, 10))\n",
"\n",
"# Generate a custom diverging colormap\n",
"cmap = sns.diverging_palette(300, 21, as_cmap=True)\n",
"\n",
"# Draw the heatmap with the mask and correct aspect ratio\n",
"sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.25, center=0,\n",
" square=False, linewidths=.5, cbar_kws={\"shrink\": .5}, annot=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7322e90450>"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NyDE9uXaTk2c"
},
"source": [
"### Split 80/20 del training set"
]
},
{
"cell_type": "code",
"metadata": {
"id": "43i-hdQVTn37"
},
"source": [
"# train_EN, test_EN = train_test_split(df[df['language'] == 'en'], test_size=0.2, train_size=0.8, random_state = 42, stratify=df[df['language'] == 'en']['task2'])\n",
"# train_ES, test_ES = train_test_split(df[df['language'] == 'es'], test_size=0.2, train_size=0.8, random_state = 42, stratify=df[df['language'] == 'es']['task2'])\n",
"\n",
"train_EN = df[df['language'] == 'en']\n",
"test_EN = df_test[df_test['language'] == 'en']\n",
"\n",
"train_ES = df[df['language'] == 'es']\n",
"test_ES = df_test[df_test['language'] == 'es']\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "lc8BYKlNVl5h"
},
"source": [
"### Baseline results"
]
},
{
"cell_type": "code",
"metadata": {
"id": "VXke3SQ2VqRR"
},
"source": [
"mapper_baseline = DataFrameMapper( [ ('text', [CountVectorizer(), TfidfTransformer()] ) ] )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "MIoG1xFNdIY3"
},
"source": [
"##### Task 1 - EN"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YemxBavZbpqP",
"outputId": "6a7ba7bd-cf42-4e7e-e26f-d68200a58c4e"
},
"source": [
"train_x = mapper_baseline.fit_transform(train_EN)\n",
"test_x = mapper_baseline.transform(test_EN)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_EN['task1'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_EN['task1'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" non-sexist 0.6606 0.7638 0.7085 1050\n",
" sexist 0.7505 0.6442 0.6933 1158\n",
"\n",
" accuracy 0.7011 2208\n",
" macro avg 0.7056 0.7040 0.7009 2208\n",
"weighted avg 0.7078 0.7011 0.7005 2208\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XSaAuj7IdMAG"
},
"source": [
"##### Task 1 - ES"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9n_-irSvVwyG",
"outputId": "4a5f8fc9-9042-4098-e52b-59d031e87471"
},
"source": [
"train_x = mapper_baseline.fit_transform(train_ES)\n",
"test_x = mapper_baseline.transform(test_ES)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_ES['task1'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_ES['task1'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" non-sexist 0.6586 0.8149 0.7284 1037\n",
" sexist 0.7811 0.6100 0.6850 1123\n",
"\n",
" accuracy 0.7083 2160\n",
" macro avg 0.7198 0.7124 0.7067 2160\n",
"weighted avg 0.7223 0.7083 0.7059 2160\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XiOj5zirdOYV"
},
"source": [
"##### Task 2 - EN"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1iGQmmGUdOs2",
"outputId": "52449567-04c0-4eeb-acdd-8c421d4c00f7"
},
"source": [
"train_x = mapper_baseline.fit_transform(train_EN)\n",
"test_x = mapper_baseline.transform(test_EN)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_EN['task2'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_EN['task2'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" ideological-inequality 0.8298 0.2342 0.3653 333\n",
"misogyny-non-sexual-violence 0.4737 0.0837 0.1423 215\n",
" non-sexist 0.5196 0.9571 0.6736 1050\n",
" objectification 0.5000 0.0867 0.1477 150\n",
" sexual-violence 0.5882 0.1515 0.2410 198\n",
" stereotyping-dominance 0.7231 0.1794 0.2875 262\n",
"\n",
" accuracy 0.5394 2208\n",
" macro avg 0.6057 0.2821 0.3096 2208\n",
" weighted avg 0.5909 0.5394 0.4550 2208\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g04_Ep_edPmd"
},
"source": [
"##### Task 2 - ES"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EzJRB-aeVyWn",
"outputId": "07b6d906-b302-49f3-f8c4-bd675089bdc3"
},
"source": [
"train_x = mapper_baseline.fit_transform(train_ES)\n",
"test_x = mapper_baseline.transform(test_ES)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_ES['task2'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_ES['task2'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" ideological-inequality 0.8800 0.3056 0.4536 288\n",
"misogyny-non-sexual-violence 0.7500 0.1868 0.2991 257\n",
" non-sexist 0.5353 0.9788 0.6921 1037\n",
" objectification 0.7692 0.1724 0.2817 174\n",
" sexual-violence 0.9167 0.0545 0.1028 202\n",
" stereotyping-dominance 0.7551 0.1832 0.2948 202\n",
"\n",
" accuracy 0.5690 2160\n",
" macro avg 0.7677 0.3135 0.3540 2160\n",
" weighted avg 0.6819 0.5690 0.4882 2160\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a2nizyEZTOoX"
},
"source": [
"### Results with only bias features"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jubSS5G6T6Yw"
},
"source": [
"#### All features"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UBiY8Zy1UCE2"
},
"source": [
"mapper_0 = DataFrameMapper( [ ('pos', [CountVectorizer(), TfidfTransformer()]), ('hashtags', [CountVectorizer(), TfidfTransformer()]), ('mention_count', None), ('hashtag_count', None), ('sentiment', None), ('assertive_verbs', None), ('factive_verbs', None), ('hedge_words', None), ('implicative_verbs', None), ('strong_subjective_words', None), ('weak_subjective_words', None), ('biased_words', None), ('distance_biased_words', None), ('ps_hurtlex', None), ('rci_hurtlex', None), ('pa_hurtlex', None), ('ddf_hurtlex', None), ('ddp_hurtlex', None), ('dmc_hurtlex', None), ('is_hurtlex', None), ('or_hurtlex', None), ('an_hurtlex', None), ('asm_hurtlex', None), ('asf_hurtlex', None), ('pr_hurtlex', None), ('om_hurtlex', None), ('qas_hurtlex', None), ('cds_hurtlex', None), ('re_hurtlex', None), ('svp_hurtlex', None) ] )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "vGC2WvFMdklx"
},
"source": [
"##### Task 1 - EN"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OT08qN-VVAFK",
"outputId": "d8b98717-627b-4b25-ff23-90227df66d32"
},
"source": [
"train_x = mapper_0.fit_transform(train_EN)\n",
"test_x = mapper_0.transform(test_EN)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_EN['task1'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_EN['task1'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" non-sexist 0.5184 0.8324 0.6389 1050\n",
" sexist 0.6628 0.2988 0.4119 1158\n",
"\n",
" accuracy 0.5525 2208\n",
" macro avg 0.5906 0.5656 0.5254 2208\n",
"weighted avg 0.5941 0.5525 0.5198 2208\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tuP7rsqldpuI"
},
"source": [
"##### Task 1 - ES"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "D1KdnIc8dq7Q",
"outputId": "e559929f-37fe-479f-ed8b-ea04b1865909"
},
"source": [
"train_x = mapper_0.fit_transform(train_ES)\n",
"test_x = mapper_0.transform(test_ES)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_ES['task1'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_ES['task1'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" non-sexist 0.5334 0.6548 0.5879 1037\n",
" sexist 0.5964 0.4711 0.5264 1123\n",
"\n",
" accuracy 0.5593 2160\n",
" macro avg 0.5649 0.5629 0.5571 2160\n",
"weighted avg 0.5661 0.5593 0.5559 2160\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ydjEW-JBdtyX"
},
"source": [
"##### Task 2 - EN"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4jbxFwf6du6e",
"outputId": "3e2599fe-3e9c-4deb-bc5d-d51968f041c8"
},
"source": [
"train_x = mapper_0.fit_transform(train_EN)\n",
"test_x = mapper_0.transform(test_EN)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_EN['task2'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_EN['task2'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" ideological-inequality 0.0000 0.0000 0.0000 333\n",
"misogyny-non-sexual-violence 0.0000 0.0000 0.0000 215\n",
" non-sexist 0.4757 0.9990 0.6445 1050\n",
" objectification 0.0000 0.0000 0.0000 150\n",
" sexual-violence 0.3333 0.0051 0.0100 198\n",
" stereotyping-dominance 0.0000 0.0000 0.0000 262\n",
"\n",
" accuracy 0.4755 2208\n",
" macro avg 0.1348 0.1673 0.1091 2208\n",
" weighted avg 0.2561 0.4755 0.3074 2208\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hzYuNd2kdxxG"
},
"source": [
"##### Task 2 - ES"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QaS4Al4FeHpN",
"outputId": "875ced29-5411-4f40-98be-91315f43da0c"
},
"source": [
"train_x = mapper_0.fit_transform(train_ES)\n",
"test_x = mapper_0.transform(test_ES)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_ES['task2'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_ES['task2'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" ideological-inequality 0.2500 0.0069 0.0135 288\n",
"misogyny-non-sexual-violence 0.3659 0.1751 0.2368 257\n",
" non-sexist 0.4845 0.9479 0.6412 1037\n",
" objectification 0.0000 0.0000 0.0000 174\n",
" sexual-violence 0.0000 0.0000 0.0000 202\n",
" stereotyping-dominance 0.0000 0.0000 0.0000 202\n",
"\n",
" accuracy 0.4769 2160\n",
" macro avg 0.1834 0.1883 0.1486 2160\n",
" weighted avg 0.3095 0.4769 0.3378 2160\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "atUCc1bhUCfm"
},
"source": [
"#### Selected features (work in progress)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "gwVkf7mdTVzn"
},
"source": [
"mapper_1 = DataFrameMapper( [ ('pos', [CountVectorizer(), TfidfTransformer()]), ('hashtags', [CountVectorizer(), TfidfTransformer()]), ('assertive_verbs', None), ('hedge_words', None), ('factive_verbs', None), ('implicative_verbs', None), ('biased_words', None), ('distance_biased_words', None), ('hashtag_count', None), ('sentiment', None), ('asm_hurtlex', None), ('asf_hurtlex', None), ('pr_hurtlex', None) ] )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RphBK-71Tc0g",
"outputId": "8d1a0ae9-a309-4cb7-cdbf-9c080c635b6b"
},
"source": [
"train_x = mapper_1.fit_transform(train)\n",
"test_x = mapper_1.transform(test)\n",
"\n",
"clf = LogisticRegression(random_state=42)\n",
"clf.fit(train_x, train['task1'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test['task1'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" non-sexist 0.5969 0.6931 0.6414 720\n",
" sexist 0.6054 0.5015 0.5485 676\n",
"\n",
" accuracy 0.6003 1396\n",
" macro avg 0.6011 0.5973 0.5950 1396\n",
"weighted avg 0.6010 0.6003 0.5964 1396\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7J0qt6A8VV1Y",
"outputId": "c9d95f72-548e-4811-ba78-267c5ecb222e"
},
"source": [
"train_x = mapper_1.fit_transform(train)\n",
"test_x = mapper_1.transform(test)\n",
"\n",
"clf = LogisticRegression(random_state=42)\n",
"clf.fit(train_x, train['task2'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test['task2'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" ideological-inequality 0.4074 0.0636 0.1100 173\n",
"misogyny-non-sexual-violence 0.3704 0.0730 0.1220 137\n",
" non-sexist 0.5299 0.9708 0.6856 720\n",
" objectification 0.6667 0.0200 0.0388 100\n",
" sexual-violence 0.3889 0.0673 0.1148 104\n",
" stereotyping-dominance 0.0000 0.0000 0.0000 162\n",
"\n",
" accuracy 0.5222 1396\n",
" macro avg 0.3939 0.1991 0.1785 1396\n",
" weighted avg 0.4369 0.5222 0.3906 1396\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "72dmlBHSVjFJ"
},
"source": [
"### Results baseline + bias features"
]
},
{
"cell_type": "code",
"metadata": {
"id": "FwZAQ54cWIyi"
},
"source": [
"# mapper_2 = DataFrameMapper( [ ('text', [CountVectorizer(), TfidfTransformer()]), ('pos', [CountVectorizer(), TfidfTransformer()]), ('hashtags', [CountVectorizer(), TfidfTransformer()]), ('hashtag_count', None), ('sentiment', None), ('assertive_verbs', None), ('hedge_words', None), ('implicative_verbs', None), ('biased_words', None), ('distance_biased_words', None), ('ps_hurtlex', None), ('or_hurtlex', None), ('an_hurtlex', None), ('asm_hurtlex', None), ('asf_hurtlex', None), ('pr_hurtlex', None), ('svp_hurtlex', None) ] )\n",
"# task 1\n",
"# mapper_2 = DataFrameMapper( [ ('text', [CountVectorizer(ngram_range=[1,2]), TfidfTransformer()]), ('pos', [CountVectorizer(), TfidfTransformer()]), ('hashtag_count', None), ('sentiment', None), ('assertive_verbs', None), ('implicative_verbs', None), ('distance_biased_words', None), ('pr_hurtlex', None), ('or_hurtlex', None), ('an_hurtlex', None), ('asm_hurtlex', None), ('asf_hurtlex', None)] )\n",
"# task 2\n",
"mapper_2 = DataFrameMapper( [ ('text', [CountVectorizer(ngram_range=[1,2]), TfidfTransformer()]), ('pos', [CountVectorizer(), TfidfTransformer()]), ('hashtag_count', None), ('sentiment', None), ('assertive_verbs', None), ('hedge_words', None), ('implicative_verbs', None), ('biased_words', None), ('distance_biased_words', None), ('pr_hurtlex', None), ('asm_hurtlex', None), ('asf_hurtlex', None)] )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "pKXJvWhaf3v6"
},
"source": [
"##### Task 1 - EN"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vbRKDedrWOSB",
"outputId": "3158c3f8-8cfd-4d4d-8d66-560a6df87767"
},
"source": [
"train_x = mapper_2.fit_transform(train_EN)\n",
"test_x = mapper_2.transform(test_EN)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_EN['task1'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_EN['task1'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" non-sexist 0.5709 0.8133 0.6709 1050\n",
" sexist 0.7247 0.4456 0.5519 1158\n",
"\n",
" accuracy 0.6205 2208\n",
" macro avg 0.6478 0.6295 0.6114 2208\n",
"weighted avg 0.6516 0.6205 0.6085 2208\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dHHa_VUhlU2m",
"outputId": "9450267e-ff68-432e-9cef-391890b43894"
},
"source": [
"test_X = mapper_2.transform(test_dataset_EN)\n",
"predicted = clf.predict(test_X)\n",
"predicted"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['sexist', 'non-sexist', 'sexist', ..., 'non-sexist', 'sexist',\n",
" 'sexist'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 35
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q7e9MBfJrwfn"
},
"source": []
},
{
"cell_type": "code",
"metadata": {
"id": "bxhYfo4Umi1I"
},
"source": [
"test_dataset_EN\n",
"test_dataset_EN['predicted'] = pd.Series(predicted, index=test_dataset_EN.index)\n",
"\n",
"testEN_with_results = test_dataset_EN[['test_case', 'id', 'predicted']]\n",
"testEN_with_results\n",
"\n",
"testEN_with_results['id'] = testEN_with_results['id'].astype(str)\n",
"testEN_with_results['id'] = testEN_with_results['id'].str.zfill(6)\n",
"asd = testEN_with_results['id'].str.zfill(6)\n",
"testEN_with_results['id'] = asd\n",
"testEN_with_results['id']\n",
"\n",
"testEN_with_results.to_csv('data.tsv', sep='\\t', header=False, index=False)\n",
"!cp data.tsv \"/content/gdrive/MyDrive/Colab Notebooks/results_task1_run1/en.tsv\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "YcKzR0JIf4Tq"
},
"source": [
"##### Task 1 - ES"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pWQPCwMKf47B",
"outputId": "49776ef3-6a3a-4217-b81c-0de92c2ce165"
},
"source": [
"train_x = mapper_2.fit_transform(train_ES)\n",
"test_x = mapper_2.transform(test_ES)\n",
"\n",
"clf = SVC(random_state=42)\n",
"clf.fit(train_x, train_ES['task1'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_ES['task1'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" non-sexist 0.6031 0.6287 0.6157 1037\n",
" sexist 0.6432 0.6180 0.6303 1123\n",
"\n",
" accuracy 0.6231 2160\n",
" macro avg 0.6232 0.6234 0.6230 2160\n",
"weighted avg 0.6240 0.6231 0.6233 2160\n",
"\n",
" precision recall f1-score support\n",
"\n",
" non-sexist 0.6031 0.6287 0.6157 1037\n",
" sexist 0.6432 0.6180 0.6303 1123\n",
"\n",
" accuracy 0.6231 2160\n",
" macro avg 0.6232 0.6234 0.6230 2160\n",
"weighted avg 0.6240 0.6231 0.6233 2160\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iiKYWHdXxvLc",
"outputId": "8f66cab2-d197-4c32-c187-218ad88fbf7b"
},
"source": [
"test_X = mapper_2.transform(test_dataset_ES)\n",
"predicted = clf.predict(test_X)\n",
"predicted"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['sexist', 'non-sexist', 'non-sexist', ..., 'non-sexist',\n",
" 'non-sexist', 'sexist'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 62
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "X-h9KyiDxwyx"
},
"source": [
"test_dataset_ES\n",
"test_dataset_ES['predicted'] = pd.Series(predicted, index=test_dataset_ES.index)\n",
"\n",
"testES_with_results = test_dataset_ES[['test_case', 'id', 'predicted']]\n",
"testES_with_results\n",
"\n",
"testES_with_results['id'] = testES_with_results['id'].astype(str)\n",
"testES_with_results['id'] = testES_with_results['id'].str.zfill(6)\n",
"asd = testES_with_results['id'].str.zfill(6)\n",
"testES_with_results['id'] = asd\n",
"testES_with_results['id']\n",
"\n",
"testES_with_results.to_csv('data.tsv', sep='\\t', header=False, index=False)\n",
"!cp data.tsv \"/content/gdrive/MyDrive/Colab Notebooks/results_task1_run1/es.tsv\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "efx4Yc6yf5jh"
},
"source": [
"##### Task 2 - EN"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AqGmKshzf5wJ",
"outputId": "d1c12e66-ea47-4d29-d960-874c97cb5cf4"
},
"source": [
"train_x = mapper_2.fit_transform(train_EN)\n",
"test_x = mapper_2.transform(test_EN)\n",
"\n",
"clf = LogisticRegression(random_state=42)\n",
"clf.fit(train_x, train_EN['task2'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_EN['task2'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" ideological-inequality 0.6797 0.3123 0.4280 333\n",
"misogyny-non-sexual-violence 0.3934 0.1116 0.1739 215\n",
" non-sexist 0.5843 0.8581 0.6952 1050\n",
" objectification 0.4667 0.1867 0.2667 150\n",
" sexual-violence 0.3171 0.4596 0.3753 198\n",
" stereotyping-dominance 0.6095 0.2443 0.3488 262\n",
"\n",
" accuracy 0.5489 2208\n",
" macro avg 0.5085 0.3621 0.3813 2208\n",
" weighted avg 0.5512 0.5489 0.5052 2208\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "01oqpqhRyn9K",
"outputId": "8cdc663c-39de-443d-d521-6ceceb08510f"
},
"source": [
"test_X = mapper_2.transform(test_dataset_EN)\n",
"predicted = clf.predict(test_X)\n",
"predicted"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['ideological-inequality', 'non-sexist', 'objectification', ...,\n",
" 'non-sexist', 'stereotyping-dominance', 'non-sexist'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 66
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jhGQ-JNHyq_z"
},
"source": [
"test_dataset_EN\n",
"test_dataset_EN['predicted'] = pd.Series(predicted, index=test_dataset_EN.index)\n",
"\n",
"testEN_with_results = test_dataset_EN[['test_case', 'id', 'predicted']]\n",
"testEN_with_results\n",
"\n",
"testEN_with_results['id'] = testEN_with_results['id'].astype(str)\n",
"testEN_with_results['id'] = testEN_with_results['id'].str.zfill(6)\n",
"asd = testEN_with_results['id'].str.zfill(6)\n",
"testEN_with_results['id'] = asd\n",
"testEN_with_results['id']\n",
"\n",
"testEN_with_results.to_csv('data.tsv', sep='\\t', header=False, index=False)\n",
"!cp data.tsv \"/content/gdrive/MyDrive/Colab Notebooks/results_task2_run1/en.tsv\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "0uhfyz8wgBcH"
},
"source": [
"##### Task 2 - ES"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LM6qX7MAWPRZ",
"outputId": "835c9d9b-9913-4f09-cc30-677a2b1b2fd2"
},
"source": [
"train_x = mapper_2.fit_transform(train_ES)\n",
"test_x = mapper_2.transform(test_ES)\n",
"\n",
"clf = LogisticRegression(random_state=42)\n",
"clf.fit(train_x, train_ES['task2'])\n",
"\n",
"predicted = clf.predict(test_x)\n",
"print(classification_report(test_ES['task2'], predicted, digits = 4))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" ideological-inequality 0.7516 0.4097 0.5303 288\n",
"misogyny-non-sexual-violence 0.5748 0.2840 0.3802 257\n",
" non-sexist 0.5557 0.9421 0.6991 1037\n",
" objectification 0.7353 0.1437 0.2404 174\n",
" sexual-violence 0.9375 0.0743 0.1376 202\n",
" stereotyping-dominance 0.6324 0.2129 0.3185 202\n",
"\n",
" accuracy 0.5792 2160\n",
" macro avg 0.6979 0.3445 0.3844 2160\n",
" weighted avg 0.6415 0.5792 0.5136 2160\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DKPnYR0nzGUi",
"outputId": "5f843a91-7ca8-42a2-a341-ad4da83d725f"
},
"source": [
"test_X = mapper_2.transform(test_dataset_ES)\n",
"predicted = clf.predict(test_X)\n",
"predicted"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['non-sexist', 'non-sexist', 'non-sexist', ..., 'non-sexist',\n",
" 'non-sexist', 'non-sexist'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 69
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iFXpI-0gzIrh"
},
"source": [
"test_dataset_ES\n",
"test_dataset_ES['predicted'] = pd.Series(predicted, index=test_dataset_ES.index)\n",
"\n",
"testES_with_results = test_dataset_ES[['test_case', 'id', 'predicted']]\n",
"testES_with_results\n",
"\n",
"testES_with_results['id'] = testES_with_results['id'].astype(str)\n",
"testES_with_results['id'] = testES_with_results['id'].str.zfill(6)\n",
"asd = testES_with_results['id'].str.zfill(6)\n",
"testES_with_results['id'] = asd\n",
"testES_with_results['id']\n",
"\n",
"testES_with_results.to_csv('data.tsv', sep='\\t', header=False, index=False)\n",
"!cp data.tsv \"/content/gdrive/MyDrive/Colab Notebooks/results_task2_run1/es.tsv\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "etvbnrl9TLRR"
},
"source": [
"### Borrar"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iYD-3LHt9eHG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7f0ac5bf-a6f0-4586-c25b-b372578f3973"
},
"source": [
"drive.mount('/content/gdrive')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/gdrive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RIcW3AvYxy99"
},
"source": [
"df = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/data/training_dataset_with_features.csv\", sep=\",\")\n",
"# df2 = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/data/test_dataset_with_features.csv\", sep=\",\")\n",
"\n",
"\n",
"df['text'] = df['text'].fillna('')\n",
"df['hashtags'] = df['hashtags'].fillna('')\n",
"# df2['text'] = df['text'].fillna('')\n",
"# df2['hashtags'] = df['hashtags'].fillna('')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "62btJlpTxzUf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 327
},
"outputId": "4fece919-e06c-412f-d8c5-93ac7c5a3dcb"
},
"source": [
"df[0:1]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>test_case</th>\n",
" <th>id</th>\n",
" <th>source</th>\n",
" <th>language</th>\n",
" <th>text</th>\n",
" <th>task1</th>\n",
" <th>task2</th>\n",
" <th>pos</th>\n",
" <th>hashtags</th>\n",
" <th>mention_count</th>\n",
" <th>hashtag_count</th>\n",
" <th>sentiment</th>\n",
" <th>assertive_verbs</th>\n",
" <th>factive_verbs</th>\n",
" <th>hedge_words</th>\n",
" <th>implicative_verbs</th>\n",
" <th>strong_subjective_words</th>\n",
" <th>weak_subjective_words</th>\n",
" <th>biased_words</th>\n",
" <th>distance_biased_words</th>\n",
" <th>ps_hurtlex</th>\n",
" <th>rci_hurtlex</th>\n",
" <th>pa_hurtlex</th>\n",
" <th>ddf_hurtlex</th>\n",
" <th>ddp_hurtlex</th>\n",
" <th>dmc_hurtlex</th>\n",
" <th>is_hurtlex</th>\n",
" <th>or_hurtlex</th>\n",
" <th>an_hurtlex</th>\n",
" <th>asm_hurtlex</th>\n",
" <th>asf_hurtlex</th>\n",
" <th>pr_hurtlex</th>\n",
" <th>om_hurtlex</th>\n",
" <th>qas_hurtlex</th>\n",
" <th>cds_hurtlex</th>\n",
" <th>re_hurtlex</th>\n",
" <th>svp_hurtlex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>EXIST2021</td>\n",
" <td>1</td>\n",
" <td>twitter</td>\n",
" <td>en</td>\n",
" <td>call anti feminazi shut fuck vile commentary e...</td>\n",
" <td>sexist</td>\n",
" <td>ideological-inequality</td>\n",
" <td>PRP VBZ PRP `` JJ '' WRB IN VB DT VBG RP IN PR...</td>\n",
" <td></td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.28925</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 test_case id ... cds_hurtlex re_hurtlex svp_hurtlex\n",
"0 0 EXIST2021 1 ... 1 0 0\n",
"\n",
"[1 rows x 38 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3eNEXAfscgkm"
},
"source": [
"test = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/data/test_dataset_with_features.csv\", sep=\",\")\n",
"\n",
"test['text'] = df['text'].fillna('')\n",
"test['hashtags'] = df['hashtags'].fillna('')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mGuMMFxUlLmP"
},
"source": [
"test_dataset_EN = test[test['language'] == 'en']\n",
"test_dataset_ES = test[test['language'] == 'es']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 209
},
"id": "H_h6--vhlqVF",
"outputId": "d70d5c9f-72fd-4028-a68b-a48bab2c1c6e"
},
"source": [
"# en = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/results_task1_run1/en.tsv\", sep=\"\\t\")\n",
"# es = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/results_task1_run1/es.tsv\", sep=\"\\t\")\n",
"# en['id'] = en['id'].astype(str)\n",
"# en['id'] = en['id'].str.zfill(6)\n",
"# asd = en['id'].str.zfill(6)\n",
"# en['id'] = asd\n",
"# # enes = pd.concat([en, es])\n",
"\n",
"# df_test.to_csv('df_test_df_test.tsv', sep='\\t')\n",
"!cp df_test_df_test.tsv \"/content/gdrive/MyDrive/Colab Notebooks/data/df_test.tsv\"\n",
"# en"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-3001fa031a0c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# df_test.to_csv('df_test_df_test.tsv', sep='\\t')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cp df_test_df_test.tsv \"/content/gdrive/MyDrive/Colab Notebooks/data/df_test.tsv\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0men\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'en' is not defined"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "i0718u3p9Ope",
"outputId": "8746d019-ceed-4028-fe6e-85d8a20013bd"
},
"source": [
"drive.mount('/content/gdrive')\n",
"df1 = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/data/EXIST2021_training.tsv\", sep=\"\\t\")\n",
"df2 = pd.read_csv(\"/content/gdrive/MyDrive/Colab Notebooks/data/EXIST2021_test.tsv\", sep=\"\\t\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/gdrive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0UPU5-LyjuCl"
},
"source": [
"df = pd.concat([df1, df2])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_dtFWGpfkLxL",
"outputId": "b284bed0-fe63-4a7e-85b6-32e80cb1ebd0"
},
"source": [
"%matplotlib inline\n",
"\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"from yellowbrick.text import TSNEVisualizer\n",
"from yellowbrick.datasets import load_hobbies\n",
"\n",
"# Load the data and create document vectors\n",
"tfidf = TfidfVectorizer()\n",
"\n",
"df1['task2'] = df1['task2'].astype('category')\n",
"\n",
"X = tfidf.fit_transform(df1.text[0:10])\n",
"y = df1.task2[0:10]\n",
"\n",
"\n",
"# Create the visualizer and draw the vectors\n",
"tsne = TSNEVisualizer()\n",
"tsne.fit(X, y)\n",
"plt.show()\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
"*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
"*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
"*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xHoycnVckTwu"
},
"source": [
"%matplotlib inline\n",
"plt.show()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XSwu_hfNl-Qj"
},
"source": [],
"execution_count": null,
"outputs": []
}
]
}
@franfj
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franfj commented May 10, 2025

MIT License

Copyright (c) 2025 Francisco Javier Rodrigo Ginés

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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