def get_mentions():
""" Get all mentions from the timeline and store them in the db.
"""
t = Twitter(auth=OAuth(Config.access_token, Config.access_token_secret, Config.api_key, Config.api_secret))
mentions = t.statuses.mentions_timeline()
for mention in mentions:
user_id = UserDao.add_user(mention["user"]["screen_name"], mention["user"]["id"])
if UserTweetDao.is_new_user_tweet(mention["id"]):
UserTweetDao.create_user_tweet(user_id, mention["id"], mention["text"], mention)
def read_filtered_stream(bwl_list):
query = ""
for i in range(number_query_items):
query += random.choice(bwl_list) + ","
query = query[:-1]
twitter_stream = TwitterStream(auth=OAuth(Config.access_token, Config.access_token_secret, Config.api_key, Config.api_secret), domain='userstream.twitter.com')
iterator = twitter_stream.statuses.filter(track=query)
i = 0
rest_calls = 0
for tweet in iterator:
if "lang" in tweet and "retweeted_status" not in tweet:
if "en" == tweet["lang"]:
if UserDao.is_new_user(tweet["user"]["id"]):
if rest_calls < 5:
rest_calls += 1
# check if user has multiple tweets with keywords
count = get_timeline_by_id(tweet["user"]["id"], bwl_list)
if count > threshold_domain_expert:
key_list = []
for key in bwl_list:
key = key.lower()
if key in tweet["text"].lower():
key_list.append(key)
recs = do_recommendation(tweet, " ".join(key_list), True)
if recs > 0:
i += 1
print("rec found")
else:
print("rest calls exhausted")
break
if i == rotate_after_hits:
break
def read_stream():
""" Listens to the user-stream and reacts to mention events with a recommendation.
"""
twitter_user_stream = TwitterStream(auth=OAuth(Config.access_token, Config.access_token_secret, Config.api_key,
Config.api_secret), domain='userstream.twitter.com')
for msg in twitter_user_stream.user():
logging.info(msg)
recommend = False
# check if the the bot was mentioned in the status update
if "entities" in msg:
for mention in msg["entities"]["user_mentions"]:
if mention["screen_name"] == Config.name.replace("@", ""):
recommend = True
if recommend:
user_id = UserDao.add_user(msg["user"]["screen_name"], msg["user"]["id"])
UserTweetDao.create_user_tweet(user_id, msg["id"], msg["text"], msg)
Recommender.get_recommendation()
distribute_recommendations()
def do_recommendation(tweet, keyword_list="", delete_fails=False):
# TODO only persist if there is a recommendation?
user = UserDao.add_user(tweet["user"]["screen_name"], tweet["user"]["id"])
nr_distributed = 0
if not UserTweetDao.is_existing_user_tweet(tweet["id"]):
if len(keyword_list) > 0:
tweet_text = keyword_list
else:
tweet_text = tweet["text"]
UserTweetDao.create_user_tweet(user.id, tweet["id"], tweet_text, tweet)
Recommender.get_recommendation()
nr_distributed = distribute_recommendations()
# TODO delete failed
# if nr_distributed == 0 and delete_fails:
#
# user.delete()
# pass
return nr_distributed
