Web Opinion Mining for Social Networking Sites

Kaur, Bishas and Saxena, Aarpit and Singh, Sanjay (2012) Web Opinion Mining for Social Networking Sites. In: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology , New York, USA .

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Web technologies provide a platform for Internet users around the world to communicate and express their opinions. Analysis of developing Web opinions is potentially valuable for discovering ongoing topics of interest like religion, politics and crime detection, understanding how topics evolve together with the underlying social interaction between participants, and identifying important participants who have great in uence in various topics of discussions. In this paper,we investigate the density-based clustering algorithm and use the scalable distance-based clustering technique for Web opinion clustering which gives more reliable and accurate results. We have conducted experiments and benchmarked with the density-based algorithm to show that the new algorithm has better performance. This Web opinion clustering technique enables the identication of themes within discussions in Web social networks their development, as well as the interactions of active participants. With the help of interactive visualization tools, we make use of the identied topic clusters to display social network development, the network topology, similarity between topics, and the similarity values between participants. Using this we can successfully compare different threads on social networking sites, extract useful information from them and identify the underlying themes of discussions.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Social Networks, Web Mining, Clustering
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 31 Jul 2014 07:01
Last Modified: 31 Jul 2014 07:01
URI: http://eprints.manipal.edu/id/eprint/140275

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