├── README.md
└── sentiment-analysis-id.py


/README.md:
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1 | Sentiment Analysis Twitter Bahasa Indonesia dengan TextBlob. Diaplikasikan untuk menganalisis tentang topik debat Capres-Cawapres 2019.
2 | Singkatnya, saya bereksperimen dengan translate tweets Bahasa Indonesia ke English agar bisa dianalisis oleh TextBlob. 
3 | Ini adalah code untuk tugas tambahan untuk mata kuliah Artificial Intelligence.
4 | 
5 | Credits:
6 | [Source code diambil dari Siraj Raval](https://github.com/llSourcell/twitter_sentiment_challenge)
7 | 


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/sentiment-analysis-id.py:
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 1 | import tweepy
 2 | from textblob import TextBlob
 3 | 
 4 | # Step 1 - Authenticate
 5 | consumer_key= 'CONSUMER_KEY'
 6 | consumer_secret= 'CONSUMER_SECRET'
 7 | 
 8 | access_token='ACCESS_TOKEN'
 9 | access_token_secret='ACCESS_TOKEN_SECRET'
10 | 
11 | auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
12 | auth.set_access_token(access_token, access_token_secret)
13 | 
14 | api = tweepy.API(auth)
15 | 
16 | #Step 2 - Retrieve Tweets
17 | public_tweets = api.search(q=['CANDIDATE_NAME', 'DebatCapres2019'], count=100)
18 | 
19 | all_polarity = 0
20 | for tweet in public_tweets:
21 |     print(tweet.text)
22 | #Step 3 - Perform Sentiment Analysis on Tweets
23 |     analysis = TextBlob(tweet.text)
24 |     an = analysis.translate(from_lang='id', to='en')
25 |     print(an.sentiment)
26 |     all_polarity += an.polarity
27 | 
28 |     print("")
29 | 
30 | #Step 4 - To Check Positive or Negative Sentiment from Overall Tweets
31 | if (all_polarity/100 > 0):
32 |     print(all_polarity/100)
33 |     print("")
34 |     print('Positive')
35 | else:
36 |     print(all_polarity/100)
37 |     print("")
38 |     print("Negative")


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