from classifiers.SentimentPolarityEmotion import SentimentPolarityEmotionClassifier
from pprint import pprint
from lymbix import Lymbix
apikey = "8a77503e17148fb5f3d5fd7d1e80ce86c23b6133"
# a few other api keys you use if this one has reached its limit
#apikey = "06bb0ef22cc21896451d7d9ed0f53eff6c99cd93"
#apikey = "1321dd7e3e05237514db9ae7812202c23f6b2f5e"
#SPEC = SentimentPolarityEmotionClassifier(apikey)
#result = SPEC.analyse("I love this movie")
#results = SPEC.analyseAll(["I love this movie", "I hate this movie"])
#pprint(results)
#print result["dominant_emotion"]
#print result["article_sentiment"]["sentiment"]
l = Lymbix(apikey)
r = l.tonalize_multiple(["I love you", "I hate you"])
print [i["article_sentiment"] for i in r]
return t
apikey = "8a77503e17148fb5f3d5fd7d1e80ce86c23b6133"
l = Lymbix(apikey)
fdbu = FinalDBUsers()
fdbt = FinalDBTweets()
users = fdbu._db.keys()
done = 0
total = len(users)
for i, uid in enumerate(users):
u = fdbu.get(uid)
texts = [_processPunctuation(t["text"]) for t in u["tweets"]]
if not fdbt.hasTweetsOfUser(uid) and len(texts) > 0:
r = l.tonalize_multiple(texts)
print uid, i, "/", total, "\t\t\t\t", r
for j, t in enumerate(u["tweets"]):
_tid = t["id"]
_text = t["text"]
_processedText = _processPunctuation(_text)
_polarity = 0
_emotion = 0
fdbt.add(tweetId=str(_tid), text=_text, processedText=_processedText, sent=r[j], userId=uid)
