COLLECTION OF WEB OPINION INFORMATION AND VISUALIZATION BASED ON SDC

Analysis of developing Web opinions is potentially valuable for discovering ongoing topics of interests of the public like terrorist and crime detection, understanding how topics evolve together with the underlying social interaction between participants, and identifying important participants who have great influence in various topics of discussions. Nonetheless, the work of analyzing and clustering Web opinions is extremely challenging. Unlike regular documents, Web opinions are short and sparse text messages with noisy content. Typical document clustering techniques with the goal of clustering all documents applied to Web opinions produce unsatisfactory performance. In this project, investigated the density-based clustering algorithm and proposed the scalable distancebased clustering technique for Web opinion clustering. This Web opinion clustering technique enables the identification of themes within discussions in Web social networks and their development, as well as the interactions of active participants. This also developed interactive visualization tools, which make use of the identified topic clusters to display social network development, the network topology similarity between topics, and the similarity values between participants.