Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach
User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through user...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/8/4066 |
| _version_ | 1827621805005733888 |
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| author | Najwa AlGhamdi Shaheen Khatoon Majed Alshamari |
| author_facet | Najwa AlGhamdi Shaheen Khatoon Majed Alshamari |
| author_sort | Najwa AlGhamdi |
| collection | DOAJ |
| description | User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users’ feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers’ opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications. |
| first_indexed | 2024-03-09T11:11:27Z |
| format | Article |
| id | doaj.art-d1fb4810f3ef427b824df6a6c8fedcce |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:11:27Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d1fb4810f3ef427b824df6a6c8fedcce2023-12-01T00:44:36ZengMDPI AGApplied Sciences2076-34172022-04-01128406610.3390/app12084066Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier ApproachNajwa AlGhamdi0Shaheen Khatoon1Majed Alshamari2Department of Information Systems, King Faisal University, Al-Ahsa 31982, Saudi ArabiaSchool of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215000, ChinaDepartment of Information Systems, King Faisal University, Al-Ahsa 31982, Saudi ArabiaUser-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users’ feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers’ opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications.https://www.mdpi.com/2076-3417/12/8/4066sentiment classificationprior knowledgetopic modelsdata labellingensemble learningstacked generalization |
| spellingShingle | Najwa AlGhamdi Shaheen Khatoon Majed Alshamari Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach Applied Sciences sentiment classification prior knowledge topic models data labelling ensemble learning stacked generalization |
| title | Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach |
| title_full | Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach |
| title_fullStr | Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach |
| title_full_unstemmed | Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach |
| title_short | Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach |
| title_sort | multi aspect oriented sentiment classification prior knowledge topic modelling and ensemble learning classifier approach |
| topic | sentiment classification prior knowledge topic models data labelling ensemble learning stacked generalization |
| url | https://www.mdpi.com/2076-3417/12/8/4066 |
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