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Repository for our Twitter Trend Detection project for CS 175 at UCI.
Explore using LDA to discover what a trend is.
An idea of a process you might use: invoke LDA on the tweets with a set of topics. LDA works by representing each topic as a proportion of words. Find the word that is most "representative" in them all (i.e. high proportion in a high number of topics).
For cleaning routines, we should adopt the interface:
tweets = cleaning_method(tweets, ...)
where tweets is the list of dictionary objects. This is because later feature extractors need the input to be untokenized.
Fix both non english and stop word removals.
Use PageRank to extract a trend out of a group of tweets.
Use the frequency of terms in large clusters to determine what the trend is.
Use Average Linkage to Cluster, see #9
See here: http://scikit-learn.org/stable/modules/clustering.html
These will be necessary for determining later features.
See #9
Add a flag to the TF feature extractor to produce a TF-IDF transform.
Make two feature extractors:
Separate removal of stop words & punctuation into 2 separate scripts.
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