Mining aspects of customer’s review on the social network
Abstract This study represents an efficient method for extracting product aspects from customer reviews and give solutions for inferring aspect ratings and aspect weights. Aspect ratings often reflect the user’s satisfaction on aspects of a product and aspect weights reflect the degree of importance...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2019-02-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-019-0184-5 |
| _version_ | 1819092470792716288 |
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| author | Tu Nguyen Thi Ngoc Ha Nguyen Thi Thu Viet Anh Nguyen |
| author_facet | Tu Nguyen Thi Ngoc Ha Nguyen Thi Thu Viet Anh Nguyen |
| author_sort | Tu Nguyen Thi Ngoc |
| collection | DOAJ |
| description | Abstract This study represents an efficient method for extracting product aspects from customer reviews and give solutions for inferring aspect ratings and aspect weights. Aspect ratings often reflect the user’s satisfaction on aspects of a product and aspect weights reflect the degree of importance of the aspects posed by the user. These tasks therefore play a very important role for manufacturers to better understand their customers’ opinion on their products and services. The study addresses the problem of aspect extraction by using aspect words based on conditional probability combined with the bootstrap technique. To infer the user’s rating for aspects, a supervised approach called the Naïve Bayes classification method is proposed to learn the aspect ratings in which sentiment words are considered as features. The weight of an aspect is estimated by leveraging the frequencies of aspect words within each review and the aspect consistency across all reviews. Experimental results show that the proposed method obtains very good performance on real world datasets in comparison with other state-of-the-art methods. |
| first_indexed | 2024-12-21T22:56:08Z |
| format | Article |
| id | doaj.art-b621767a2968410392d668acf575f965 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-21T22:56:08Z |
| publishDate | 2019-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-b621767a2968410392d668acf575f9652022-12-21T18:47:26ZengSpringerOpenJournal of Big Data2196-11152019-02-016112110.1186/s40537-019-0184-5Mining aspects of customer’s review on the social networkTu Nguyen Thi Ngoc0Ha Nguyen Thi Thu1Viet Anh Nguyen2Department of E-Commerce, Vietnam Electric Power UniversityDepartment of E-Commerce, Vietnam Electric Power UniversityInstitute of Information Technology, Vietnam Academy of Science and TechnologyAbstract This study represents an efficient method for extracting product aspects from customer reviews and give solutions for inferring aspect ratings and aspect weights. Aspect ratings often reflect the user’s satisfaction on aspects of a product and aspect weights reflect the degree of importance of the aspects posed by the user. These tasks therefore play a very important role for manufacturers to better understand their customers’ opinion on their products and services. The study addresses the problem of aspect extraction by using aspect words based on conditional probability combined with the bootstrap technique. To infer the user’s rating for aspects, a supervised approach called the Naïve Bayes classification method is proposed to learn the aspect ratings in which sentiment words are considered as features. The weight of an aspect is estimated by leveraging the frequencies of aspect words within each review and the aspect consistency across all reviews. Experimental results show that the proposed method obtains very good performance on real world datasets in comparison with other state-of-the-art methods.http://link.springer.com/article/10.1186/s40537-019-0184-5Aspect extractionAspect ratingAspect weightConditional probabilityCore termNaive Bayes |
| spellingShingle | Tu Nguyen Thi Ngoc Ha Nguyen Thi Thu Viet Anh Nguyen Mining aspects of customer’s review on the social network Journal of Big Data Aspect extraction Aspect rating Aspect weight Conditional probability Core term Naive Bayes |
| title | Mining aspects of customer’s review on the social network |
| title_full | Mining aspects of customer’s review on the social network |
| title_fullStr | Mining aspects of customer’s review on the social network |
| title_full_unstemmed | Mining aspects of customer’s review on the social network |
| title_short | Mining aspects of customer’s review on the social network |
| title_sort | mining aspects of customer s review on the social network |
| topic | Aspect extraction Aspect rating Aspect weight Conditional probability Core term Naive Bayes |
| url | http://link.springer.com/article/10.1186/s40537-019-0184-5 |
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