Python function,files and dictionaries

Project 1-lizapiya-Probem1

##To start, define a function called strip_punctuation which takes one parameter, a string which represents a word, and removes characters considered punctuation from everywhere in the word. (Hint: remember the .replace() method for strings.)


punctuation_chars = ["'", '"', ",", ".", "!", ":", ";", '#', '@']

def strip_punctuation(word):
    New_word=""  
    print("Word is: ",word)
    for w in word:
        if w not in punctuation_chars:
            New_word=New_word+w
    return New_word

Project 1-lizapiya-Probem2

### Next, copy in your strip_punctuation function and define a function called get_pos which takes one parameter, a string which represents one or more sentences, and calculates how many words in the string are considered positive words. Use the list, positive_words to determine what words will count as positive. The function should return a positive integer - how many occurrences there are of positive words in the text. Note that all of the words in positive_words are lower cased, so you’ll need to convert all the words in the input string to lower case as well.


punctuation_chars = ["'", '"', ",", ".", "!", ":", ";", '#', '@']
# list of positive words to use
positive_words = []
with open("positive_words.txt") as pos_f:
    for lin in pos_f:
        if lin[0] != ';' and lin[0] != '\n':
            positive_words.append(lin.strip())

def strip_punctuation(word):
    New_word=""  
    print("Word is: ",word)
    for w in word:
        if w not in punctuation_chars:
            New_word=New_word+w
    return  New_word


def get_pos(sentences):
    lower_string=sentences.lower()                    ## As the list of positive words are in lower case
    str_punc_removed=strip_punctuation(lower_string)  ##For Punctuation Removal
    print("str_punc_removed:",str_punc_removed)
    splitted_string=str_punc_removed.split()          ## For splitting the sentence into words
    print ("splitted_string:",splitted_string)
    
    
    pos_count=0
    for w in splitted_string:
        if w in positive_words:
            pos_count=pos_count+1
    return pos_count

Project 1-lizapiya-Probem3

### Next, copy in your strip_punctuation function and define a function called get_neg which takes one parameter, a string which represents one or more sentences, and calculates how many words in the string are considered negative words. Use the list, negative_words to determine what words will count as negative. The function should return a positive integer - how many occurrences there are of negative words in the text. Note that all of the words in negative_words are lower cased, so you’ll need to convert all the words in the input string to lower case as well.


punctuation_chars = ["'", '"', ",", ".", "!", ":", ";", '#', '@']

negative_words = []
with open("negative_words.txt") as pos_f:
    for lin in pos_f:
        if lin[0] != ';' and lin[0] != '\n':
            negative_words.append(lin.strip())
            

def strip_punctuation(word):
    New_word=""  
    print("Word is: ",word)
    for w in word:
        if w not in punctuation_chars:
            New_word=New_word+w
    return  New_word


def get_neg(sentences):
    lower_string=sentences.lower()                    ## As the list of positive words are in lower case
    str_punc_removed=strip_punctuation(lower_string)  ## For Punctuation Removal
    print("str_punc_removed:",str_punc_removed)
    splitted_string=str_punc_removed.split()          ## For splitting the sentence into words
    print ("splitted_string:",splitted_string)
    
    
    neg_count=0
    for w in splitted_string:
        if w in negative_words:
            neg_count=neg_count+1
    return neg_count

Project 1-lizapiya-Sentiment Classifier

Finally, copy in your previous functions and write code that opens the file project_twitter_data.csv which has the fake generated twitter data (the text of a tweet, the number of retweets of that tweet, and the number of replies to that tweet). Your task is to build a sentiment classifier, which will detect how positive or negative each tweet is. Copy the code from the code windows above, and put that in the top of this code window. Now, you will write code to create a csv file called resulting_data.csv, which contains the Number of Retweets, Number of Replies, Positive Score (which is how many happy words are in the tweet), Negative Score (which is how many angry words are in the tweet), and the Net Score (how positive or negative the text is overall) for each tweet. The file should have those headers in that order. Remember that there is another component to this project. You will upload the csv file to Excel or Google Sheets and produce a graph of the Net Score vs Number of Retweets. Check Coursera for that portion of the assignment, if you’re accessing this textbook from Coursera.

punctuation_chars = ["'", '"', ",", ".", "!", ":", ";", '#', '@']
# lists of words to use
positive_words = []
with open("positive_words.txt") as pos_f:
    for lin in pos_f:
        if lin[0] != ';' and lin[0] != '\n':
            positive_words.append(lin.strip())


negative_words = []
with open("negative_words.txt") as pos_f:
    for lin in pos_f:
        if lin[0] != ';' and lin[0] != '\n':
            negative_words.append(lin.strip())

def strip_punctuation(word):
    New_word=""  
    #print("Word is: ",word)
    for w in word:
        if w not in punctuation_chars:
            New_word=New_word+w
    return  New_word


def get_pos(sentences):
    lower_string=sentences.lower()                    ## As the list of positive words are in lower case
    str_punc_removed=strip_punctuation(lower_string)  ##For Punctuation Removal
    #print("str_punc_removed:",str_punc_removed)
    splitted_string=str_punc_removed.split()          ## For splitting the sentence into words
    #print ("splitted_string:",splitted_string)
    
    
    pos_count=0
    for w in splitted_string:
        if w in positive_words:
            pos_count=pos_count+1
    return pos_count

def get_neg(sentences):
    lower_string=sentences.lower()                    ## As the list of positive words are in lower case
    str_punc_removed=strip_punctuation(lower_string)  ## For Punctuation Removal
    #print("str_punc_removed:",str_punc_removed)
    splitted_string=str_punc_removed.split()          ## For splitting the sentence into words
    #print ("splitted_string:",splitted_string)
    
    
    neg_count=0
    for w in splitted_string:
        if w in negative_words:
            neg_count=neg_count+1
    return neg_count




#The following code opens the twitter file
fileref=open("project_twitter_data.csv","r")
data = fileref.readlines()

#The following code writes in the csv file named resulting_data
outfile=open("resulting_data.csv","w")
outfile.write("Number of Retweets, Number of Replies, Positive Score, Negative Score, Net Score")
outfile.write("\n")

for i in data[1:]:
    res_row=""
    splt=i.strip().split(",")           #Leading and trailing whitespaces are removed with strip
    res_row=("{},{},{},{},{}".format(splt[1], splt[2], get_pos(splt[0]), get_neg(splt[0]), (get_pos(splt[0])-get_neg(splt[0]))))
    outfile.write(res_row)
    outfile.write("\n")

outfile.close()

Scatter-plot

import pandas as pd
import matplotlib.pyplot as plt
df=pd.read_csv("Project 1-Sentiment Classifier.csv")
fig,ax=plt.subplots()
my_scatter_plot=ax.scatter(df[" Net Score"],df["Number of Retweets"])
plt.show()

Thanks for the help here, I corrected some of my mistakes using your code.
The scatter plot should have been labeled, e.g.:
ax.set_title('Number of Retweets vs the Net Score')
ax.set_xlabel('Net Score')
ax.set_ylabel('Retweets')

Hi ma'am, can you please help me how to perform the "Sentiment Classifier" project.