This study is in the area of sentiment classification: classifying online review documents according to the overall sentiment expressed in them. This paper presents a prototype sentiment-based meta search engine that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended or non-recommended information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents. It does this by using an automatic classifier based on a supervised machine learning algorithm, Support Vector Machine (SVM). This paper also discusses various issues we have encountered during the prototype development, and presents our approaches for resolving them. A user evaluation of the prototype was carried out with positive responses from users.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/106241 |
| Date | January 2006 |
| Creators | Na, Jin-Cheon, Khoo, Christopher S.G., Chan, Syin |
| Contributors | Khoo, C., Singh, D., Chaudhry, A.S. |
| Publisher | School of Communication & Information, Nanyang Technological University |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Conference Paper |
Page generated in 0.0021 seconds