Summary of the paper
| Title | Fine-grained Opinion Topic and Polarity Identification |
|---|---|
| Authors | Xiwen Cheng and Feiyu Xu |
| Abstract | This paper presents OMINE, an opinion mining system which aims to identify concepts such as products and their attributes, and analyze their corresponding polarities. Our work pioneers at linking extracted topic terms with domain-specific concepts. Compared with previous work, taking advantage of ontological techniques, OMINE achieves 10% higher recall with the same level precision on the topic extraction task. In addition, making use of opinion patterns for sentiment analysis, OMINE improves the performance of the backup system (NGram) around 6% for positive reviews and 8% for negative ones. |
| Language | |
| Topics | Information Extraction, Information Retrieval, Text mining, Acquisition, Machine Learning |
Full paper |
Fine-grained Opinion Topic and Polarity Identification |
Slides |
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| Bibtex | @InProceedings{CHENG08.678, author = {Xiwen Cheng and Feiyu Xu}, title = {Fine-grained Opinion Topic and Polarity Identification}, booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, year = {2008}, month = {may}, date = {28-30}, address = {Marrakech, Morocco}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-4-0}, note = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |