def classify(self, results):
"""Classifies the results set by adding a "tag" attribute to each of the results.
The same set of results are returned, with additional statistics and tagging.
Each result gets one of the tags :), :( or :|
And a stats section is added.
@return an annotated array of results.
[{"tag": "pos",
"iso_language_code": "en",
"text": "@chucklelate im not that excited about google voice. although it seems neat, i dont see myself using it.",
"created_at": "Sat, 14 Mar 2009 00:00:03 +0000",
"profile_image_url": "http:\/\/s3.amazonaws.com\/twitter_production\/profile_images\/80373954\/IMG_0008_normal.JPG",
"to_user": "******",
"source": "<a href="http:\/\/twitter.com\/">web<\/a>",
"from_user": "******",
"from_user_id": 5160745,
"to_user_id": 409063,
"id": 1324759664},...],
"""
c = BayesianClassifier()
c.train(db.fetch_all_tweets())
for result in results:
tag = c.classify(result['text'])
result['tag'] = tag
return results
def classify(self, results):
"""Classifies the results set by adding a "tag" attribute to each of the results.
The same set of results are returned, with additional statistics and tagging.
Each result gets one of the tags :), :( or :|
And a stats section is added.
@return an annotated array of results.
[{"tag": "pos",
"iso_language_code": "en",
"text": "@chucklelate im not that excited about google voice. although it seems neat, i dont see myself using it.",
"created_at": "Sat, 14 Mar 2009 00:00:03 +0000",
"profile_image_url": "http:\/\/s3.amazonaws.com\/twitter_production\/profile_images\/80373954\/IMG_0008_normal.JPG",
"to_user": "******",
"source": "<a href="http:\/\/twitter.com\/">web<\/a>",
"from_user": "******",
"from_user_id": 5160745,
"to_user_id": 409063,
"id": 1324759664},...],
"""
c = BayesianClassifier()
c.train(db.fetch_all_tweets())
for result in results:
tag = c.classify(result['text'])
result['tag'] = tag
return results
def classify(self, results):
c = BayesianClassifier()
c.train(db.fetch_all_status())
for result in results:
message = "%s" % result.get('message')
comments = result.get('comments')
tag = c.classify(message)
x = {str('sentiment'):str(tag)}
result.update(x)
if 'data' in comments:
for cm in comments.get('data'):
comment = "%s" % cm.get('message')
c_tag = c.classify(comment)
cm.update(sentiment=c_tag)
return results
