Аналіз даних із соцмереж у Python
Alex Hanna
Computational Social Scientist
SentimentIntensityAnalyzer()
nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer sid = SentimentIntensityAnalyzer() sentiment_scores = tweets['text'].apply(sid.polarity_scores)
from nltk.sentiment.vader import SentimentIntensityAnalyzer
sid = SentimentIntensityAnalyzer()
sentiment_scores = tweets['text'].apply(sid.polarity_scores)
tweet1 = 'RT @jeffrey_heer: Thanks for inviting me, and thanks for the lovely visualization of the talk! ...' print(sid.polarity_scores(tweet1))
{'neg': 0.0, 'neu': 0.496, 'pos': 0.504, 'compound': 0.9041}
tweet2 = 'i am having problems with google play music' print(sid.polarity_scores(tweet2)
{'neg': 0.267, 'neu': 0.495, 'pos': 0.238, 'compound': -0.0772}
sentiment = sentiment_scores.apply(lambda x: x['compound']) sentiment_fb = sentiment[check_word_in_tweet('facebook', tweets)] .resample('1 min').mean() sentiment_gg = sentiment[check_word_in_tweet('google', tweets)] .resample('1 min').mean()
sentiment = sentiment_scores.apply(lambda x: x['compound'])
sentiment_fb = sentiment[check_word_in_tweet('facebook', tweets)] .resample('1 min').mean() sentiment_gg = sentiment[check_word_in_tweet('google', tweets)] .resample('1 min').mean()
plt.plot( sentiment_fb.index.minute, sentiment_fb, color = 'blue' ) plt.plot( sentiment_g.index.minute, sentiment_gg, color = 'green' ) plt.xlabel('Minute') plt.ylabel('Sentiment') plt.title('Sentiment of companies') plt.legend(('Facebook', 'Google')) plt.show()
Preparing Video For Download...
