Review on an Enhanced LDA Topic Model Approach for Event Extraction from Twitter

Authors

  • Zarana Patel  PG Student, Department of Computer Engineering, Government Engineering College Gandhinagar, Gandhinagar, Gujarat, India
  • Jitendra Dhobi  Associate Professor, Department of Computer Engineering, Government Engineering College Gandhinagar, Gandhinagar, Gujarat, India

Keywords:

Topic modeling, Event Extraction, Twitter, LDA, Data mining

Abstract

Topic models are powerful tools to identify latent text patterns in the content. They are applied in a wide range of areas including event extraction from Twitter. Twitter, as a popular micro blogging service, has become a new information channel for users to receive and exchange the important information on current events. Tweets recently gain a lot of importance due to its ability of produce information rapidly. Tweets are commonly related to some events. In this paper provide you a review on event extraction from twitter using topic modeling. Based on the study of the researchers LDA is the best topic model for event extraction. Though applying traditional LDA topic model directly on tweets posses two challenges: 1) Data scarceness problem due to the nature of short text length of the tweets. 2) Generated summaries contain words that are somewhat general and independent to the topic that is failed to understand the semantic of twitter data. Event Extraction methods present in this literature address this problem and classify different approach and discuss commonly used features.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-10-30

Issue

Volume 3, Issue 7, September-October-2018

Section

Research Articles

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How to Cite

[1]
Zarana Patel, Jitendra Dhobi, " Review on an Enhanced LDA Topic Model Approach for Event Extraction from Twitter , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.352-358, September-October-2018.