An improved deep learning-based approach for sentiment mining
The sentiment mining approaches can typically be divided into lexicon and machine learning approaches. Recently there are an increasing number of approaches which combine both to improve the performance when used separately. However, this still lacks contextual understanding which led to the introdu...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/56111/ http://psasir.upm.edu.my/id/eprint/56111/1/An%20improved%20deep%20learning-based%20approach%20for%20sentiment%20mining.pdf |
| _version_ | 1848852990523342848 |
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| author | Mohd Sharef, Nurfadhlina Shafazand, Mohammad Yaser |
| author_facet | Mohd Sharef, Nurfadhlina Shafazand, Mohammad Yaser |
| author_sort | Mohd Sharef, Nurfadhlina |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The sentiment mining approaches can typically be divided into lexicon and machine learning approaches. Recently there are an increasing number of approaches which combine both to improve the performance when used separately. However, this still lacks contextual understanding which led to the introduction of deep learning approaches which allows for semantic compositionality over a sentiment treebank. This paper enhances the deep learning approach with semantic lexicon so that scores can be computed in-stead merely nominal classification. Besides, neutral classification is also improved. Results suggest that the approach outperforms its original. |
| first_indexed | 2025-11-15T10:46:51Z |
| format | Conference or Workshop Item |
| id | upm-56111 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:46:51Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-561112017-07-03T09:36:39Z http://psasir.upm.edu.my/id/eprint/56111/ An improved deep learning-based approach for sentiment mining Mohd Sharef, Nurfadhlina Shafazand, Mohammad Yaser The sentiment mining approaches can typically be divided into lexicon and machine learning approaches. Recently there are an increasing number of approaches which combine both to improve the performance when used separately. However, this still lacks contextual understanding which led to the introduction of deep learning approaches which allows for semantic compositionality over a sentiment treebank. This paper enhances the deep learning approach with semantic lexicon so that scores can be computed in-stead merely nominal classification. Besides, neutral classification is also improved. Results suggest that the approach outperforms its original. IEEE 2014 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/56111/1/An%20improved%20deep%20learning-based%20approach%20for%20sentiment%20mining.pdf Mohd Sharef, Nurfadhlina and Shafazand, Mohammad Yaser (2014) An improved deep learning-based approach for sentiment mining. In: 2014 4th World Congress on Information and Communication Technologies (WICT 2014), 8-11 Dec. 2014, Melaka, Malaysia. (pp. 344-348). 10.1109/WICT.2014.7077291 |
| spellingShingle | Mohd Sharef, Nurfadhlina Shafazand, Mohammad Yaser An improved deep learning-based approach for sentiment mining |
| title | An improved deep learning-based approach for sentiment mining |
| title_full | An improved deep learning-based approach for sentiment mining |
| title_fullStr | An improved deep learning-based approach for sentiment mining |
| title_full_unstemmed | An improved deep learning-based approach for sentiment mining |
| title_short | An improved deep learning-based approach for sentiment mining |
| title_sort | improved deep learning-based approach for sentiment mining |
| url | http://psasir.upm.edu.my/id/eprint/56111/ http://psasir.upm.edu.my/id/eprint/56111/ http://psasir.upm.edu.my/id/eprint/56111/1/An%20improved%20deep%20learning-based%20approach%20for%20sentiment%20mining.pdf |