Topic Flow Model: A Graph Theoretic Temporal Topic Model for Noisy Mediums

Abstract

In the modern era, data is being created faster than ever. Social media, in par-

ticular, churns out hundreds of millions of short documents a day. It would be useful

to understand the underlying topics being discussed on popular channels of social

media, and how those discussions evolve over time. There exist state of the art topic

models that accurately classify texts large and small, but few attempt to follow topics

through time, and many are adversely affected by the large amount of noise in social

media documents. We propose Topic Flow Model (TFM), a graph theoretic temporal

topic model that identifies topics as they emerge, and tracks them through time as

they persist, diminish, and re-emerge. TFM identifies topic words by capturing the

changing relationship strength of words over time, and offers solutions for dealing with

flood words, i.e., domain specific words that pollute topics. We conduct an extensive

empirical analysis of TFM on Twitter data, newspaper articles, and synthetic data

and find that the topic accuracy and signal to noise ratio are better than state of the

art methods.