Detecting deceptive reviews using lexical and syntactic features
Deceptive opinion classification has attracted a lot of research interest due to the rapid growth of social media users. Despite the availability of a vast number of opinion features and classification techniques, review classification still remains a challenging task. In this work we applied stylom...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE (IEEEXplore)
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41326/ |
| _version_ | 1848849665552809984 |
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| author | Shojaee, Somayeh Azmi Murad, Masrah Azrifah Azman, Azreen Mohd Sharef, Nurfadhlina Nadali, Samaneh |
| author_facet | Shojaee, Somayeh Azmi Murad, Masrah Azrifah Azman, Azreen Mohd Sharef, Nurfadhlina Nadali, Samaneh |
| author_sort | Shojaee, Somayeh |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Deceptive opinion classification has attracted a lot of research interest due to the rapid growth of social media users. Despite the availability of a vast number of opinion features and classification techniques, review classification still remains a challenging task. In this work we applied stylometric features, i.e. lexical and syntactic, using supervised machine learning classifiers, i.e. Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) and Naive Bayes, to detect deceptive opinion. Detecting deceptive opinion by a human reader is a difficult task because spammers try to write wise reviews, therefore it causes changes in writing style and verbal usage. Hence, considering the stylometric features help to distinguish the spammer writing style to find deceptive reviews. Experiments on an existing hotel review corpus suggest that using stylometric features is a promising approach for detecting deceptive opinions. |
| first_indexed | 2025-11-15T09:54:00Z |
| format | Conference or Workshop Item |
| id | upm-41326 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:54:00Z |
| publishDate | 2013 |
| publisher | IEEE (IEEEXplore) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-413262015-11-04T02:02:51Z http://psasir.upm.edu.my/id/eprint/41326/ Detecting deceptive reviews using lexical and syntactic features Shojaee, Somayeh Azmi Murad, Masrah Azrifah Azman, Azreen Mohd Sharef, Nurfadhlina Nadali, Samaneh Deceptive opinion classification has attracted a lot of research interest due to the rapid growth of social media users. Despite the availability of a vast number of opinion features and classification techniques, review classification still remains a challenging task. In this work we applied stylometric features, i.e. lexical and syntactic, using supervised machine learning classifiers, i.e. Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) and Naive Bayes, to detect deceptive opinion. Detecting deceptive opinion by a human reader is a difficult task because spammers try to write wise reviews, therefore it causes changes in writing style and verbal usage. Hence, considering the stylometric features help to distinguish the spammer writing style to find deceptive reviews. Experiments on an existing hotel review corpus suggest that using stylometric features is a promising approach for detecting deceptive opinions. IEEE (IEEEXplore) 2013 Conference or Workshop Item NonPeerReviewed Shojaee, Somayeh and Azmi Murad, Masrah Azrifah and Azman, Azreen and Mohd Sharef, Nurfadhlina and Nadali, Samaneh (2013) Detecting deceptive reviews using lexical and syntactic features. In: 2013 13th International Conference on Intelligent Systems Design and Applications (ISDA), 8-10 Dec. 2013, Bangi, Selangor, Malaysia. (pp. 53-58). 10.1109/ISDA.2013.6920707 |
| spellingShingle | Shojaee, Somayeh Azmi Murad, Masrah Azrifah Azman, Azreen Mohd Sharef, Nurfadhlina Nadali, Samaneh Detecting deceptive reviews using lexical and syntactic features |
| title | Detecting deceptive reviews using lexical and syntactic features |
| title_full | Detecting deceptive reviews using lexical and syntactic features |
| title_fullStr | Detecting deceptive reviews using lexical and syntactic features |
| title_full_unstemmed | Detecting deceptive reviews using lexical and syntactic features |
| title_short | Detecting deceptive reviews using lexical and syntactic features |
| title_sort | detecting deceptive reviews using lexical and syntactic features |
| url | http://psasir.upm.edu.my/id/eprint/41326/ http://psasir.upm.edu.my/id/eprint/41326/ |