def marvel_vs_dc(co_1, co_2): both = [co_1, co_2] avg_score_1 = dict() avg_score_2 = dict() for co in both: count = 0 for movie in co: data = t.search(q="movie", count=50) for status in data['statuses']: count += 1 status = str(status) score = SentimentIntensityAnalyzer().polarity_scores(status) if co == co_1: for key, value in score.items(): avg_score_1[key] = (avg_score_1.get(key, 0) + value) if co == co_2: for key, value in score.items(): avg_score_2[key] = (avg_score_1.get(key, 0) + value) count = count for key, value in avg_score_1.items(): avg_score_1[key] = (value / count) for key, value in avg_score_2.items(): avg_score_2[key] = (value / count) print("Dc has a score of {}".format(avg_score_1)) print("Marvel has a score of {}".format(avg_score_2)) if avg_score_1['pos'] - avg_score_1['neg'] < avg_score_2[ 'pos'] - avg_score_2['neg']: print("Marvel Wins!") elif avg_score_1['pos'] - avg_score_1['neg'] == avg_score_2[ 'pos'] - avg_score_2['neg']: print("They Tie.") else: print("DC Wins!")
import argparse from nltk.sentiment.vader import SentimentIntensityAnalyzer import operator if __name__ == '__main__': parser = argparse.ArgumentParser(description='Vader sentiment analyzer') parser.add_argument('-s', action='store', type=str) args = parser.parse_args() if args.s: scores = SentimentIntensityAnalyzer().polarity_scores(args.s) del (scores['compound']) sorted_scores = sorted(scores.items(), key=operator.itemgetter(1), reverse=True) print(sorted_scores[0][0])
#Python model for analyzing the sentiment of our sentences #Python package that allows us to pass cmd line arguments to our script import argparse from nltk.sentiment.vader import SentimentIntensityAnalyzer import operator #entry point of python script if __name__ == '__main__': parser = argparse.ArgumentParser(description='Vader sentiment analyzer') parser.add_argument('-s', action='store', type=str) args = parser.parse_args( ) #all parameters that we pass to our script wil be here if args.s: scores = SentimentIntensityAnalyzer().polarity_scores( args.s) #how pos/neg/neutral inputted sentence is # {'neg': 0.756, 'neu': 0.244, 'pos': 0.0, 'compound': -0.4767} del scores['compound'] #we dont need it #sort and take highest value sorted_scores = sorted( scores.items(), key=operator.itemgetter(1), reverse=True) #itemgetter(1) is value. itemgetter(0) is key, 'neg' # sorted_scores = [('neg', 0.756), ('neu', 0.244), ('pos', 0.0)] print sorted_scores[0][0] #it'll return neg, neu or pos
import argparse from nltk.sentiment.vader import SentimentIntensityAnalyzer import operator if __name__ == "__main__": parser = argparse.ArgumentParser(description="Vader sentiment analyzer") parser.add_argument("-s",action="store",type=str) args = parser.parse_args() if args.s: scores = SentimentIntensityAnalyzer().polarity_scores(args.s) del scores["compound"] sorted_scores = sorted(scores.items(), key=operator.itemgetter(1), reverse=True) print sorted_scores