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
