emotions.py

import sys
import re
import matplotlib.pyplot as plt
import nltk
from utilities import cleanText 
from nltk.sentiment.vader import SentimentIntensityAnalyzer

sentiment_analyzer = SentimentIntensityAnalyzer() # Our Great Sentiment Analyzer

def analyze(name):
    linesList = cleanText(name + '.txt')
    neutral, negative, positive = 0, 0, 0

    for index, sentence in enumerate(linesList):
        print("Processing {0}%".format(str((index * 100) / len(linesList))))
        
        # Ignore Emoji
        if re.match(r'^[\w]', sentence):
            continue
        
        scores = sentiment_analyzer.polarity_scores(sentence)
        
        # We don't need that component
        scores.pop('compound', None)
        
        maxAttribute = max(scores, key=lambda k: scores[k])

        if maxAttribute == "neu":
            neutral += 1
        elif maxAttribute == "neg":
            negative += 1
        else:
            positive += 1

    total = neutral + negative + positive
    print("Negative: {0}% | Neutral: {1}% | Positive: {2}%".format(
        negative*100/total, neutral*100/total, positive*100/total))
    
    # Plot
    #### Code Omitted ####

analyze(sys.argv[1])