Learning knowledge from relevant webpage for opinion analysis

Ruifeng Xu, Kam Fai Wong, Qin Lu, Yunqing Xia, Wenjie Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

21 Citations (Scopus)


T This paper presents an opinion analysis system based on linguistic knowledge which is acquired from small-scale annotated text and raw topic-relevant webpage. Based on the observation on the annotated opinion corpus, some word-, collocation-and sentence-level linguistic features for opinion analysis are discovered. Supervised and unsupervised learning techniques are developed to learn these features from annotated text and raw relevant webpage, respectively. These features are then incorporated into a support vector machine based classifier to identify opinionated sentences from running text and determine their polarities. Evaluations show that the proposed opinion analysis system, namely OA, achieved promising performance, which shows the effectiveness of linguistic knowledge learning from relevant webpage.
Original languageEnglish
Title of host publicationProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Number of pages7
Publication statusPublished - 1 Dec 2008
Event2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 - Sydney, NSW, Australia
Duration: 9 Dec 200812 Dec 2008


Conference2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
CitySydney, NSW

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

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