def TweetRank(self, unidentified_terms):
ranked_tweets = defaultdict(list)
# Create an instance of the indexer
indexer = Indexer()
# Index the tweets
indexer.LoadIndexes()
for term in unidentified_terms:
# Get the tweets for the read term
term_tweetids = indexer.GetTweetsForTerm(term)
# The setup is that the number of retweets, favorites and author followers need to be normalized
# Normalisation needs maximum and minimum of each of the tweet metrics
# First loop finds the maximum and minimum value of all metrics. Let's declare them
max_rt = 0 # will hold max value
min_rt = 100000 # will hold min value
max_fav = 0 # will hold max value
min_fav = 100000 # will hold min value
max_af = 0 # will hold max value
min_af = 100000 # will hold min value
for tweetid in term_tweetids:
tweetid_rt = self.GetRetweetsForTweetid(indexer, tweetid)
if tweetid_rt > max_rt:
max_rt = tweetid_rt
if tweetid_rt < min_rt:
min_rt = tweetid_rt
tweetid_fav = self.GetFavsForTweetid(indexer, tweetid)
if tweetid_fav > max_fav:
max_fav = tweetid_fav
if tweetid_fav < min_fav:
min_fav = tweetid_fav
tweetid_af = self.GetFollowersForTweetid(indexer, tweetid)
if tweetid_af > max_af:
max_af = tweetid_af
if tweetid_af < min_af:
min_af = tweetid_af
# Second loop uses the retrieved max and min of each metric to calculate a normalized score
# of each tweet for that term
for tweetid in term_tweetids:
# For every tweet id get the number of retweets, favorites and author followers
rt = self.GetRetweetsForTweetid(indexer, tweetid)
fav = self.GetFavsForTweetid(indexer, tweetid)
af = self.GetFollowersForTweetid(indexer, tweetid)
tweet_term_score = self.GetScoreForTermTweetid(rt, max_rt, min_rt, fav, max_fav, min_fav,
af, max_af, min_af)
ranked_tweets[term].append((tweetid, tweet_term_score))
# Get the rankings, sorted descending on score and return only last x results
self.sorted_cropped_rankings = self.SortCropRankings(ranked_tweets, unidentified_terms)
self.rankings_output = list()
for ut in unidentified_terms:
number_rankings = len(self.sorted_cropped_rankings[ut])
for i in range(0, number_rankings):
self.rankings_output.append(self.sorted_cropped_rankings[ut][i])
return self.rankings_output
