ET-LDA: Joint topic modeling for aligning events and their twitter feedback

Yuheng Hu, Ajita John, Fei Wang, Subbarao Kambhampati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations

Abstract

During broadcast events such as the Superbowl, the U.S. Presidential and Primary debates, etc., Twitter has become the de facto platform for crowds to share perspectives and commentaries about them. Given an event and an associated large-scale collection of tweets, there are two fundamental research problems that have been receiving increasing attention in recent years. One is to extract the topics covered by the event and the tweets; the other is to segment the event. So far these problems have been viewed separately and studied in isolation. In this work, we argue that these problems are in fact inter-dependent and should be addressed together. We develop a joint Bayesian model that performs topic modeling and event segmentation in one unified framework. We evaluate the proposed model both quantitatively and qualitatively on two large-scale tweet datasets associated with two events from different domains to show that it improves significantly over baseline models.

Original languageEnglish (US)
Title of host publicationAAAI-12 / IAAI-12 - Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference
Pages59-65
Number of pages7
StatePublished - Nov 7 2012
Event26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 - Toronto, ON, Canada
Duration: Jul 22 2012Jul 26 2012

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
CountryCanada
CityToronto, ON
Period7/22/127/26/12

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Hu, Y., John, A., Wang, F., & Kambhampati, S. (2012). ET-LDA: Joint topic modeling for aligning events and their twitter feedback. In AAAI-12 / IAAI-12 - Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference (pp. 59-65). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1).