An Approach for Sentiment Based Product-feature Diversification of User Generated Reviews. | [Journal of Applied and Emerging Sciences • 2018]

Author(s):

1. Nasir Naveed: Department of Computer Science and Information Technology, Virtual University Islamabad, Pakistan

2. Thomas Gottron: Institute WeST, University of Koblenz, Germany,

3. Zahid Rauf: Department of Electrical Engineering, Faculty of Information and Communication Technology, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan

Abstract:

Information shared online by the web users, is increasingly becoming a good source for others to learn from it. In many cases, the shared information is reflecting users' experiences and opinions about events or use of certain products. The volume of this shared information is huge. It is humanly very time consuming to read all this information and make an informed decision. The challenge is to analyze shared contents automatically, find dimensions of discussions, associated opinions and summarize them so that the user can have an extensive overview of the information for decision making. The research work presented in this paper addresses this challenge of information diversification by using probabilistic topic modeling and opinion extraction techniques. The proposed method automatically extracts dimensions of a particular discussion and combines it with the opinions presented in the discussion for information diversification purpose. Experiments on the real-world dataset indicates that our method is able to extract dimensions of a discussion and successfully associate it with the opinions expressed against these dimensions. The method presents users with both positive and negative opinions against a certain discussion to give an overview of the discussion.

Page(s): 96-101

DOI: DOI not available

Published: Journal: Journal of Applied and Emerging Sciences , Volume: 8, Issue: 1, Year: 2018

Keywords:

Keywords are not available for this article.

References:

[1] Eirinaki M, Pisal S, Singh J. (2011). Feature-Based Opinion Mining and Ranking. Journal of Computer and System Sciences 78:1175-1184.

[2] Guo H, Zhu H, Guo Z, Zhang X, Su Z. (2009). Product Feature Categorization with Multilevel Latent Semantic Association. In Proc. of the 18th ACM conference on Information and knowledge management (CIKM '09). ACM, New York, NY, USA, pp 1087-1096.

[3] Krestel R, Dokoohaki N. (2011). Diversifying Product Review Rankings: Getting the Full Picture. In Proc. of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology 138-145:1.

[4] Naveed N, Gottron T, Kunegis J, Che Alhadi A. (2011). Bad News Travel Fast: A Content-Based Analysis of Interestingness on Twitter. In Proc. of the 3rd International Conference on Web Science. Koblenz, Germany. June 15 - 17, 2011.

[5] Qiu L, Zhang W, Hu C, Zhao K. (2009). SELC: A Self-Supervised Model for Sentiment Classification. In Proc. of the 18th ACM conference on Information and knowledge management (CIKM '09). ACM, New York, NY, USA, 929-936.

[6] Shi L, MingYu J, Li, Shi Qi, Mingyu Ji. (2011). A DFM Model of Mining Product Features from Customer Reviews. In Proc. of the International Conference on Control, Automation and Systems Engineering (CASE). pp 1-5.

[7] Tsaparas P, Ntoulas A,Terzi E. (2011). Selecting a Comprehensive Set of Reviews. In Proc. of the ACM international conference on Knowledge discovery and data mining, New York, NY, USA, 2011. ACM

[8] Zhai Z, Liu B, Xu H, Jia P. (2011). Constrained LDA for Grouping Product Features in Opinion Mining. In Proc. of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I (PAKDD'11), Joshua Zhexue Huang, Longbing Cao, and Jaideep Srivastava (Eds.), Vol. Part I. Springer-Verlag, Berlin, Heidelberg, pp 448-459.

Citations

Downloads

Views