Enhancing Business Intelligence by Means of Suggestive Reviews
Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumer...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/879323 |
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| _version_ | 1832548481403715584 |
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| author | Atika Qazi Ram Gopal Raj Muhammad Tahir Erik Cambria Karim Bux Shah Syed |
| author_facet | Atika Qazi Ram Gopal Raj Muhammad Tahir Erik Cambria Karim Bux Shah Syed |
| author_sort | Atika Qazi |
| collection | DOAJ |
| description | Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers’ choices and designers’ understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons. |
| format | Article |
| id | doaj-art-9c727a0bdfc7429fbcbd3e14ac4f6e0d |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-9c727a0bdfc7429fbcbd3e14ac4f6e0d2025-02-03T06:13:57ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/879323879323Enhancing Business Intelligence by Means of Suggestive ReviewsAtika Qazi0Ram Gopal Raj1Muhammad Tahir2Erik Cambria3Karim Bux Shah Syed4Faculty of Computer Science and Information Technology, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, MalaysiaFaculty of Computer Science and Information Technology, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, MalaysiaFaculty of Information Science and Technology, COMSATS Institute of Information Technology (CIIT), Park Road, Islamabad 44000, PakistanSchool of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, SingaporeFaculty of Business and Accountancy, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, MalaysiaAppropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers’ choices and designers’ understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.http://dx.doi.org/10.1155/2014/879323 |
| spellingShingle | Atika Qazi Ram Gopal Raj Muhammad Tahir Erik Cambria Karim Bux Shah Syed Enhancing Business Intelligence by Means of Suggestive Reviews The Scientific World Journal |
| title | Enhancing Business Intelligence by Means of Suggestive Reviews |
| title_full | Enhancing Business Intelligence by Means of Suggestive Reviews |
| title_fullStr | Enhancing Business Intelligence by Means of Suggestive Reviews |
| title_full_unstemmed | Enhancing Business Intelligence by Means of Suggestive Reviews |
| title_short | Enhancing Business Intelligence by Means of Suggestive Reviews |
| title_sort | enhancing business intelligence by means of suggestive reviews |
| url | http://dx.doi.org/10.1155/2014/879323 |
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