CS50 pset6 Sentiments/smile

analyzer.py

import nltk

class Analyzer():
    """Implements sentiment analysis."""
    
    def __init__(self, positives, negatives):
        """Initialize Analyzer."""
        
        # load list of positive words in memory
        self.positives = set()
        file1 = open(positives, "r")
        for line in file1:
            if not line.startswith(";") and not line.startswith("\n"):
                self.positives.add(line.strip("\n"))
        file1.close()
        
        # load list of negative words in memory
        self.negatives = set()
        file2 = open(negatives, "r")
        for line in file2:
            if not line.startswith(";") and not line.startswith("\n"):
                self.negatives.add(line.strip("\n"))
        file2.close()
        
        

    def analyze(self, text):
        """Analyze text for sentiment, returning its score."""
        # declare a variable to store total score of a given text(list of strings)
        self.total_score=0
        
        # instantiate TweetTokenizer and tokenize given text
        tokenizer = nltk.tokenize.TweetTokenizer()
        tokens = tokenizer.tokenize(text)
        
        # iterate over each string in tokens and checking if it is in
        # the list of positive or negative words
        for word in tokens:
            if str.lower(word) in self.positives:
                score = 1
            elif str.lower(word) in self.negatives:
                score = -1
            else:
                score = 0
                        
            self.total_score+=score
                    
        return self.total_score
                

Smile.py

#!/usr/bin/env python3

# a program that categorizes a word as positive or negative

import os
import sys

from analyzer import Analyzer
from termcolor import colored

def main():

    # ensure proper usage
    if len(sys.argv) != 2:
        sys.exit("Usage: ./smile word")

    # absolute paths to lists
    # Dr. Minqing Hu and Prof. Bing Liu of the University of Illinois at Chicago 
    # kindly put together lists of 2006 positive words and 4783 negative words
    # https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon
    positives = os.path.join(sys.path[0], "positive-words.txt")
    negatives = os.path.join(sys.path[0], "negative-words.txt")

    # instantiate analyzer
    analyzer = Analyzer(positives, negatives)

    # analyze word
    score = analyzer.analyze(sys.argv[1])
    if score > 0.0:
        print(colored(":)", "green"))
    elif score < 0.0:
        print(colored(":(", "red"))
    else:
        print(colored(":|", "yellow"))

if __name__ == "__main__":
    main()