social-champion-identification/main_twitter.py at master · dasUtsav/social-champion-identification · GitHub

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import os
from classes.TwitterGraph import TwitterGraph
from classes.TopicInfluence import TopicInfluence
from classes.MOI import MOI
from Instances import twitterInstance
from topic_modeling import LDAModeling


twitterGraph = TwitterGraph("twitterGraph.pickle")

# twitterGraph.add_candidate('nasw', 10, 5)

max_followers = 15
max_tweets = 10
max_follower_friends = 5

if os.path.isfile("twitterGraph.pickle"):
    twitterGraph.load_pickle()
else:
    twitterGraph.add_candidate('nasw', max_followers, max_follower_friends)

screen_names = ["nasw", "meganneiljourno", "laurakatebanks", "mike_salter"]

# for screen_name in screen_names:
#     twitterGraph.add_candidate(screen_name, max_followers, max_follower_friends)
## Run this if you wanna reset the db
# twitterGraph.resetRefetch()

## Run in order to reset the tweet_doc prop
# twitterGraph.reset_prop('tweet_doc', [])

topicInfluence = TopicInfluence(twitterGraph)

candidates = []

for screen_name in screen_names:
    res = twitterInstance.api.get_user(screen_name)
    candidates.append(res.id)

ranks = topicInfluence.compute_rank(max_tweets, candidates)

moi = MOI(twitterGraph)

for candidate in candidates:
    result = filter(lambda rank: rank['node'] == candidate, ranks)
    for res in result:
        res["moiScore"] = moi.fetch_moi_score(candidate, max_tweets)

for rank in ranks:
    twitterGraph.G.node[rank['node']]['moiScore'] = rank['moiScore']
    twitterGraph.G.node[rank['node']]['influence'] = rank['influence']

twitterGraph.write_pickle()

# twitterGraph.write_pickle()

# tweet_doc = twitterGraph.fetch_preprocessed_tweets(candidates[0], max_tweets)

# ldamodel = LDAModeling("ldamodel.pickle")
# ldamodel.loadPickle()

# ldamodel.train(tweet_doc, num_topics=10, num_passes=15)

# ldamodel.saveAsPickle()