def main():
logger.info('Starting follow neighbour script')
try:
db.init_db()
lat, lon = config.TWEET_HOME_GEO
radius = '100km'
geocode = ','.join([str(lat), str(lon), radius])
registered_users = [
user.user_id for user in db.session.query(User).all()]
results = getTweets([config.TWEET_LOCATION.split(',')[0]],
result_type='recent')
near_tweets = []
for tweet in results:
if tweet.get('place'):
if tweet.get('place').get('full_name') == 'San Francisco, CA':
near_tweets.append(tweet)
near_tweets[0]
authors = [tweet.get('user') for tweet in near_tweets]
authors_id_list = set([author.get('id_str') for author in authors])
follow_list = [author for author in authors_id_list
if author not in registered_users]
logger.info('Found %d new neighbours' % len(follow_list))
followUsers(follow_list[:30])
except Exception, err:
logger.error('%s: %s' % (Exception, err))
def main():
# Set keys found at https://apps.twitter.com
consumer_key = '1AeMVu48Xly5oNzqfy4GLpngI'
consumer_secret = 'kKqqxu7vxuvxdFkWqRgTPOyQSqmNimGNZ2MbWLvoLJo0Ug3nX3'
access_token_key = '48263654-b6Th8oJ3IuR3TeBo3FC1dKl1Avz8hBjx6WIKhW5yU'
access_token_secret = '7TnBPtIASTq1HVmYZG5ceeMSPPFocH74VJE1fKEzCAPqZ'
# Create instance of twitter.Api
print("*************************************")
print(' Initializing Twitter API...')
print("*************************************")
api = twitter.Api(consumer_key, consumer_secret, access_token_key,
access_token_secret)
print('Twitter API Initialized!')
print('\n')
# Verfy Credentials
print("**********************************")
print(' Verifying Credentials...')
print("**********************************")
print(api.VerifyCredentials())
print('\n')
# Fetch Trump Tweets
print("**********************************")
print(" Get Trump Tweets...")
print("**********************************")
trumpTweets = tweets.getTweets(api)
print('\n')
# Compare to novel
print("*******************************")
print(" Comparing to Novel...")
print("*******************************")
txtFile = open('1984.txt', 'r')
_1984Text = txtFile.read()
similarQuotes = tweets.compare(trumpTweets, _1984Text, percentMatch)
print('\n')
# Matches
print("*****************")
print(" Matches")
print("*****************")
for quote in similarQuotes:
print(quote.text)
def main():
try:
first_sleep = randrange(INITIAL_SLEEP_MAX)
logger.info('Starting retweet script, but first sleeping %d minutes'
% first_sleep)
sleep(60*first_sleep)
# Get tweets
tweets = getTweets(config.TWEET_LIKES,
result_type='mixed')
# Analyze tweets
analyzed = []
db.init_db()
retweets = [tweet.tweet_id for
tweet in db.session.query(ReTweet).all()]
for tweet in tweets:
if tweet.get('id_str') not in retweets:
analyzed.append((analysis.sentiment(tweet.get('text')),
tweet.get('id_str')
)
)
analyzed = sorted(analyzed, key=itemgetter(0), reverse=True)
# Retweet the most positive tweets
retweet_count = randrange(RETWEET_COUNT_MAX)
logger.info('Retweeting %d tweets' % retweet_count)
for i in range(retweet_count):
tweet = analyzed[i]
reTweet(tweet[1])
new_retweet = ReTweet()
new_retweet.tweet_id = tweet[1]
new_retweet.sentiment = tweet[0]
new_retweet.retweet_date = datetime.datetime.now()
db.session.add(new_retweet)
db.session.commit()
sleep_time = randrange(TWEET_SLEEP_MAX)
logger.info(
'Retweeting %d/%d tweet_id: %s, with sentiment %s and sleeping'
' for %d minutes' % (i+1,
retweet_count,
analyzed[0][1],
analyzed[0][0],
sleep_time))
sleep(60*sleep_time)
except Exception, err:
logger.error('%s: %s' % (Exception, err))
import filter, tweets, sentiment, sys from sys import argv if len(argv) == 3: # Get search term and count of tweets to be analyzed from command line arguments dataset = tweets.getTweets(argv[1], argv[2]) # Call function to get sentiment print sentiment.Sentiment(dataset) else: print "Usage: python main.py <search term> <no. of tweets to analyze>"
import tweets as twt
from curator import DataCurator
tweets = twt.getTweets()
dc = DataCurator(tweets)
clean_data = dc.basicCleanUp().removeRepeatedPunctuations().removePunctuationsAndReTweet()\
.expandPunctuatedWords().expandShortHands().build()
f = open('cleaned_data.txt', 'w', 1024)
for tweet in clean_data:
f.write("%s\n" % (tweet))
f.close()
def analyze(term, count): tweets = getTweets(term, count) return Sentiment(tweets)