def get_sentiment(company_id, text):
alchemyapi = AlchemyAPI()
key_phrases = []
for apikey in engine.get_random_alchemy_credentials():
alchemyapi.apikey = apikey
response = alchemyapi.keywords('text', text, {'sentiment': 1})
if response['status'] == 'OK':
if len(response['keywords']) == 0:
return 0
# related_words = models.RelatedWord.query.filter_by(company_id=company_id).all()
for keyword in response["keywords"]:
if 'sentiment' in keyword:
if keyword['sentiment'].has_key('score'):
key_phrases.append(float(keyword['sentiment']['score']))
elif keyword['sentiment']['type'] == 'neutral':
key_phrases.append(0)
if len(key_phrases) == 0:
return 0
else:
return float("{0:.2f}".format(sum(key_phrases)/len(key_phrases)))
elif response['status'] == 'ERROR' and response['statusInfo'] != 'unsupported-text-language':
print "ERROR: getting sentiment " + response['statusInfo']
# Skip onto the next api key
continue
else:
print "None of the above " + response['statusInfo']
return 0
#Return none when all api keys are exhausted
return None
def get_sentiment(text):
alchemyapi = AlchemyAPI()
for key in utils.get_random_alchemy_credentials():
alchemyapi.apikey = key
response = alchemyapi.sentiment("text", text)
if 'docSentiment' not in response:
continue
return response['docSentiment'].get('score', '0')
def store_concepts(tweets):
# Convert string array to string
all_tweets_as_string = ' '.join(tweets)
alchemyapi = AlchemyAPI()
alchemyapi.apikey = get_random_alchemy_credentials()
response = alchemyapi.concepts('text', all_tweets_as_string)
if response['status'] == 'OK':
for concept in response['concepts']:
concepts.append(concept['text'])
def get_sentiment(text):
alchemyapi = AlchemyAPI()
alchemyapi.apikey = get_random_alchemy_credentials()
response = alchemyapi.keywords('text', text, {'sentiment': 1})
relevances = []
if 'keywords' not in response or len(response['keywords']) == 0:
return None
for keyword in response["keywords"]:
for company_word in concepts:
if company_word.lower() in text.lower() and 'sentiment' in keyword:
if 'score' in keyword['sentiment']:
relevances.append(float(keyword['sentiment']['score']))
elif keyword['sentiment']['type'] == 'neutral':
relevances.append(0.5)
if not relevances:
return 0.5
else:
return float("{0:.2f}".format(sum(relevances)/len(relevances)))
def generate_concepts_for_company(company_id, tweets):
all_tweets_as_string = ' '.join(tweets)
alchemyapi = AlchemyAPI()
api_error = False
for apikey in engine.get_random_alchemy_credentials():
alchemyapi.apikey = apikey
response = alchemyapi.concepts('text', all_tweets_as_string)
related_words = []
if response['status'] == 'OK':
for concept in response['concepts']:
related_words.append(concept['text'])
elif response['status'] == 'ERROR' and tweets != []:
print "ERROR getting concepts" + response['statusInfo']
api_error = True
# Move onto the next api key
continue
# Return null when all api keys are exhausted
if api_error and len(related_words) == 0:
return None
return related_words
urllib3.contrib.pyopenssl.inject_into_urllib3()
# Import the necessary methods from tweepy library
from tweepy.streaming import StreamListener
from tweepy import OAuthHandler
from tweepy import Stream
import json
import re, string
#API Keys
import keys
# AlchemyAPI
from alchemyapi import AlchemyAPI
alchemyapi = AlchemyAPI()
alchemyapi.apikey = keys.alchemy_apikey
#TwitterAPI keys
consumer_key = keys.twitter_consumer_key
consumer_secret = keys.twitter_consumer_secret
access_token = keys.twitter_access_token
access_token_secret = keys.twitter_access_token_secret
# Tweet object
class Tweet:
def __init__(self, author, text, location):
self.author = author
self.text = text
self.location = location
