Aspect-based sentiment analysis on multi-domain reviews through word embedding
The finest resource for consumers to evaluate products is online product reviews, and finding such reviews that are accurate and helpful can be difficult. These reviews may sometimes be corrupted, biased, contradictory, or lacking in detail. This opens the door for customer-focused review analysis m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-06-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2023-0001 |
| _version_ | 1797795080989310976 |
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| author | Venu Gopalachari Mukkamula Gupta Sangeeta Rakesh Salakapuri Jayaram Dharmana Venkateswara Rao Pulipati |
| author_facet | Venu Gopalachari Mukkamula Gupta Sangeeta Rakesh Salakapuri Jayaram Dharmana Venkateswara Rao Pulipati |
| author_sort | Venu Gopalachari Mukkamula |
| collection | DOAJ |
| description | The finest resource for consumers to evaluate products is online product reviews, and finding such reviews that are accurate and helpful can be difficult. These reviews may sometimes be corrupted, biased, contradictory, or lacking in detail. This opens the door for customer-focused review analysis methods. A method called “Multi-Domain Keyword Extraction using Word Vectors” aims to streamline the customer experience by giving them reviews from several websites together with in-depth assessments of the evaluations. Using the specific model number of the product, inputs are continuously grabbed from different e-commerce websites. Aspects and key phrases in the reviews are properly identified using machine learning, and the average sentiment for each keyword is calculated using context-based sentiment analysis. To precisely discover the keywords in massive texts, word embedding data will be analyzed by machine learning techniques. A unique methodology developed to locate trustworthy reviews considers several criteria that determine what makes a review credible. The experiments on real-time data sets showed better results compared to the existing traditional models. |
| first_indexed | 2024-03-13T03:12:28Z |
| format | Article |
| id | doaj.art-fb37f481bd1444b1a3ccf6f84bd5c788 |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-03-13T03:12:28Z |
| publishDate | 2023-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-fb37f481bd1444b1a3ccf6f84bd5c7882023-06-26T10:46:37ZengDe GruyterJournal of Intelligent Systems2191-026X2023-06-013211020925110.1515/jisys-2023-0001Aspect-based sentiment analysis on multi-domain reviews through word embeddingVenu Gopalachari Mukkamula0Gupta Sangeeta1Rakesh Salakapuri2Jayaram Dharmana3Venkateswara Rao Pulipati4Chaitanya Bharathi Institute of Technology, Hyderabad 500075, Telangana, IndiaChaitanya Bharathi Institute of Technology, Hyderabad 500075, Telangana, IndiaChaitanya Bharathi Institute of Technology, Hyderabad 500075, Telangana, IndiaChaitanya Bharathi Institute of Technology, Hyderabad 500075, Telangana, IndiaVNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad 500075, Telangana, IndiaThe finest resource for consumers to evaluate products is online product reviews, and finding such reviews that are accurate and helpful can be difficult. These reviews may sometimes be corrupted, biased, contradictory, or lacking in detail. This opens the door for customer-focused review analysis methods. A method called “Multi-Domain Keyword Extraction using Word Vectors” aims to streamline the customer experience by giving them reviews from several websites together with in-depth assessments of the evaluations. Using the specific model number of the product, inputs are continuously grabbed from different e-commerce websites. Aspects and key phrases in the reviews are properly identified using machine learning, and the average sentiment for each keyword is calculated using context-based sentiment analysis. To precisely discover the keywords in massive texts, word embedding data will be analyzed by machine learning techniques. A unique methodology developed to locate trustworthy reviews considers several criteria that determine what makes a review credible. The experiments on real-time data sets showed better results compared to the existing traditional models.https://doi.org/10.1515/jisys-2023-0001aspect-based sentiment analysisproduct reviewscold startsentiment analysisword embedding |
| spellingShingle | Venu Gopalachari Mukkamula Gupta Sangeeta Rakesh Salakapuri Jayaram Dharmana Venkateswara Rao Pulipati Aspect-based sentiment analysis on multi-domain reviews through word embedding Journal of Intelligent Systems aspect-based sentiment analysis product reviews cold start sentiment analysis word embedding |
| title | Aspect-based sentiment analysis on multi-domain reviews through word embedding |
| title_full | Aspect-based sentiment analysis on multi-domain reviews through word embedding |
| title_fullStr | Aspect-based sentiment analysis on multi-domain reviews through word embedding |
| title_full_unstemmed | Aspect-based sentiment analysis on multi-domain reviews through word embedding |
| title_short | Aspect-based sentiment analysis on multi-domain reviews through word embedding |
| title_sort | aspect based sentiment analysis on multi domain reviews through word embedding |
| topic | aspect-based sentiment analysis product reviews cold start sentiment analysis word embedding |
| url | https://doi.org/10.1515/jisys-2023-0001 |
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