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

Authors(2) :-Zarana Patel, Jitendra Dhobi

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 352-358
Manuscript Number : CSEIT183765
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Zarana Patel, Jitendra Dhobi, "Review on an Enhanced LDA Topic Model Approach for Event Extraction from Twitter ", International 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.
Journal URL : http://ijsrcseit.com/CSEIT183765

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