Coupling topic modelling in opinion mining for social media analysis

Zhou, Xujuan and Tao, Xiaohui and Rahman, Md Mostafijur and Zhang, Ji (2017) Coupling topic modelling in opinion mining for social media analysis. In: 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2017), 23-26 Aug 2017, Leipzig, Germany.

Abstract

Many of social media platforms such as Facebook and Twitter make it easy for everyone to share their thoughts on literally anything. Topic and opinion detection in social media facilitates the identification of emerging societal trends, analysis of public reactions to policies and business products. In this paper, we proposed a new
method that combines the opining mining and context-based topic modelling to analyse public opinions on social media data. Context based topic modelling is used to categorise data in groups and discover hidden communities in data group. The unwanted data group discovered by the topic model then will be discarded. A lexicon based opinion mining method will be applied to the remaining data
groups to spot out the public sentiment about the entities. A set of Tweets data on Australian Federal Election 2010 was used in our experiments. Our experimental results demonstrate that, with the help of topic modelling, our social media analysis model is accurate and effective.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright © 2017 by the Association for Computing Machinery, Inc. Permanent restricted access to PV in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 25 Oct 2017 02:46
Last Modified: 22 May 2018 03:58
Uncontrolled Keywords: opinion mining, topic modelling, social media analysis, online social networks
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1145/3106426.3106459
URI: http://eprints.usq.edu.au/id/eprint/33166

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