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A Temporal Topic Model for Noisy Mediums

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10938)

Abstract

Social media and online news content are increasing rapidly. The goal of this work is to identify the topics associated with this content and understand the changing dynamics of these topics over time. 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. An extensive empirical analysis of TFM on Twitter data, newspaper articles, and synthetic data shows that the topic accuracy and SNR of meaningful topic words are better than the existing state.

Keywords

Temporal Topic Models Word Flood Domain-specific Words Ground Truth Topics Previous Time Window 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported by the Massive Data Institute (MDI) at Georgetown University.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Georgetown UniversityWashington, D.C.USA

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