A state of the art opinion mining and its application domains
This paper critically evaluates existing work, presents an opinion mining framework and exposes new areas of research in opinion mining. Individuals, businesses and government can now easily know the general opinion prevailing on a product, company or public policy. At the core of this field is sema...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Computer Society
2009
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| Subjects: | |
| Online Access: | http://portal.acm.org/citation.cfm?id=1586179 http://hdl.handle.net/20.500.11937/41616 |
| _version_ | 1848756194605268992 |
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| author | Binali, Haji Potdar, Vidyasagar Wu, Chen |
| author2 | Yousef Ibrahim |
| author_facet | Yousef Ibrahim Binali, Haji Potdar, Vidyasagar Wu, Chen |
| author_sort | Binali, Haji |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper critically evaluates existing work, presents an opinion mining framework and exposes new areas of research in opinion mining. Individuals, businesses and government can now easily know the general opinion prevailing on a product, company or public policy. At the core of this field is semantic orientation of subjective terms in documents or reviews which seeks to establish their contextual connotation through opinion mining. Overall item sentiment can be expressed based on its sentiment words in general or by specifically identifying its features and the opinions being expressed about them. This leads us to the motivation of the framework for opinion mining and categorizing current literature in such a manner as to make clear, research opportunities. The freedom offered by the web as a platform for presenting opinions on any subject brings with it many new opportunities. |
| first_indexed | 2025-11-14T09:08:20Z |
| format | Conference Paper |
| id | curtin-20.500.11937-41616 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:08:20Z |
| publishDate | 2009 |
| publisher | IEEE Computer Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-416162017-10-02T02:27:10Z A state of the art opinion mining and its application domains Binali, Haji Potdar, Vidyasagar Wu, Chen Yousef Ibrahim data mining unsupervised learning supervised learning Opinion mining This paper critically evaluates existing work, presents an opinion mining framework and exposes new areas of research in opinion mining. Individuals, businesses and government can now easily know the general opinion prevailing on a product, company or public policy. At the core of this field is semantic orientation of subjective terms in documents or reviews which seeks to establish their contextual connotation through opinion mining. Overall item sentiment can be expressed based on its sentiment words in general or by specifically identifying its features and the opinions being expressed about them. This leads us to the motivation of the framework for opinion mining and categorizing current literature in such a manner as to make clear, research opportunities. The freedom offered by the web as a platform for presenting opinions on any subject brings with it many new opportunities. 2009 Conference Paper http://hdl.handle.net/20.500.11937/41616 http://portal.acm.org/citation.cfm?id=1586179 IEEE Computer Society fulltext |
| spellingShingle | data mining unsupervised learning supervised learning Opinion mining Binali, Haji Potdar, Vidyasagar Wu, Chen A state of the art opinion mining and its application domains |
| title | A state of the art opinion mining and its application domains |
| title_full | A state of the art opinion mining and its application domains |
| title_fullStr | A state of the art opinion mining and its application domains |
| title_full_unstemmed | A state of the art opinion mining and its application domains |
| title_short | A state of the art opinion mining and its application domains |
| title_sort | state of the art opinion mining and its application domains |
| topic | data mining unsupervised learning supervised learning Opinion mining |
| url | http://portal.acm.org/citation.cfm?id=1586179 http://hdl.handle.net/20.500.11937/41616 |