def sentiment_analytical_processor(unique_id, social_medium, size=10):
if social_medium == 'twitter':
connection = pika.BlockingConnection()
channel = connection.channel()
tweets = []
count = 0
for method_frame, properties, body in channel.consume(unique_id):
print type (body)
tweets.append(json.loads(body)['text'])
print 'body %s' % body
print 'properties %s' % properties
print 'method_frame %s' % method_frame
count += 1
# Acknowledge the message
channel.basic_ack(method_frame.delivery_tag)
# Escape out of the loop after 10 messages
if count == size:
break
print 'tweets string' , tweets
tweets_str = ', '.join(tweets)
# Cancel the consumer and return any pending messages
requeued_messages = channel.cancel()
print 'Requeued %i messages' % requeued_messages
data = sa.sentiment(tweets_str)
print data
return data
def _calculate_sentiment(post_id, comment_string):
full_comment_str = ''
for j in filtered_comments:
for comment in j['comments']:
full_comment_str += ' '
full_comment_str += comment['message'].encode('UTF8')
logger.debug(full_comment_str)
data_ = sa.sentiment(full_comment_str)
full_comment_str = ''
data_['post_id'] = post_id
analyzed_data.append(data_)
def sentiment_analysis(params):
try:
tokens = ast.literal_eval(params.token)
except ValueError:
tokens = params.token
source = str(params.source)
try:
limit = params.limit
except AttributeError:
limit = ''
try:
since = params.since
except AttributeError:
since = ''
try:
until = params.until
except AttributeError:
until = ''
try:
post_ids = ast.literal_eval(params.post_ids)
except AttributeError:
post_ids = None
page = 'me'
try:
page = str(params.page)
except AttributeError:
pass
try:
hash_tag = str(params.hash_tag)
except AttributeError:
hash_tag = ''
#analyzed_data = 'Incorrect datasource name provided!'
if source == 'twitter':
api = SMAuth.tweepy_auth(tokens['consumer_key'], tokens['consumer_secret'], tokens['access_token'], tokens['access_token_secret'])
data_ = Tw.hashtag_search(api,hash_tag)
#lsi.initialize_stream(hash_tag, unique_id, tokens) # if already exits do something
#analyzed_data = smlf.process_social_media_data(unique_id, hash_tag)
data = sa.sentiment(data_)
result = cmg.format_response(True,data,'Data successfully processed!')
return result
elif source == 'facebook':
try:
data = FB.get_page_posts_comments(tokens, limit, since, until, page, post_ids)
except ValueError, err:
data = cmg.format_response(False,err,'Error validating access token: This may be because the user logged out or may be due to a system error.',sys.exc_info())
return data
#full_comment = []
analyzed_data = []
def _calculate_sentiment(post_id, comment_string):
full_comment_str = ''
for j in filtered_comments:
for comment in j['comments']:
full_comment_str += ' '
full_comment_str += comment['message'].encode('UTF8')
logger.debug(full_comment_str)
data_ = sa.sentiment(full_comment_str)
full_comment_str = ''
data_['post_id'] = post_id
analyzed_data.append(data_)
threads = []
if post_ids is not None:
for post_id in post_ids:
filtered_comments = filter(lambda d: d['post_id'] in post_id, data)
t = threading.Thread(target=_calculate_sentiment, args=(post_id, filtered_comments))
t.start()
print "Thread started to calculate sentiment analysis for post_id: {0}".format(post_id)
threads.append(t)
#full_comment_str.join(full_comment)
#analysed_data = sa.sentiment(full_comment_str.join(filtered_comments))
for t in threads:
try:
t.join()
except Exception, err:
print err
data = cmg.format_response(True,analyzed_data,'Data successfully processed!')
return data
for t in threads:
try:
t.join()
except Exception, err:
print err
data = cmg.format_response(True,analyzed_data,'Data successfully processed!')
return data
else:
for post in data:
full_comment_str = ''
for comments in post['comments']:
full_comment_str +=' '
full_comment_str += comments['message']
analysed_data = sa.sentiment(full_comment_str)
data = cmg.format_response(True,analysed_data,'Data successfully processed!')
return data
def build_bi_partite(params):
token= ast.literal_eval(params.token)
#source = str(params.source)
try:
limit = params.limit
except AttributeError:
limit = ''
try:
since = params.since
except AttributeError:
since = ''