Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine
One of the e-commerce in Indonesia is Shopee. Feedback from users is needed to improve the quality of e-commerce services and user satisfaction. This research process includes data scraping, labeling, text pre-processing, TF-IDF, aspect, and sentiment classification. The novelty of this research is...
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| Format: | Article |
| Language: | English |
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Udayana University, Institute for Research and Community Services
2025-01-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/105965 |
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| author | Dyah Ayu Wulandari Fitra A. Bachtiar Indriati Indriati |
| author_facet | Dyah Ayu Wulandari Fitra A. Bachtiar Indriati Indriati |
| author_sort | Dyah Ayu Wulandari |
| collection | DOAJ |
| description | One of the e-commerce in Indonesia is Shopee. Feedback from users is needed to improve the quality of e-commerce services and user satisfaction. This research process includes data scraping, labeling, text pre-processing, TF-IDF, aspect, and sentiment classification. The novelty of this research is using the SVM method with SGD to classify Indonesian language application reviews based on aspect categories consisting of 7 dimensions of service quality and sentiment so that the website created in this research can display the aspects and sentiments of the input reviews. This research also builds an Indonesian normalization dictionary to optimize the terms used to increase model accuracy. The test in aspect classification resulted in a precision value of 90%, recall of 88.73%, accuracy of 88.57%, and f1-score of 89%. Meanwhile, the sentiment classification resulted in a precision value of 96.15%, recall of 91.91%, accuracy of 94.28%, and f1-score of 93.98%. In addition, the test results (accuracy, f1-score, precision, recall) show that the lemmatization process is better than stemming and term weighting using the TF-IDF method is better than other methods (raw-term frequency, log-frequency weighting, binary-term weighting). |
| format | Article |
| id | doaj-art-c9456bc6671141b785172044acd6470e |
| institution | Kabale University |
| issn | 2088-1541 2541-5832 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj-art-c9456bc6671141b785172044acd6470e2025-01-31T23:56:26ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322025-01-0115029911110.24843/LKJITI.2024.v15.i02.p03105965Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector MachineDyah Ayu Wulandari0Fitra A. Bachtiar1Indriati Indriati2Universitas BrawijayaUniversitas BrawijayaUniversitas BrawijayaOne of the e-commerce in Indonesia is Shopee. Feedback from users is needed to improve the quality of e-commerce services and user satisfaction. This research process includes data scraping, labeling, text pre-processing, TF-IDF, aspect, and sentiment classification. The novelty of this research is using the SVM method with SGD to classify Indonesian language application reviews based on aspect categories consisting of 7 dimensions of service quality and sentiment so that the website created in this research can display the aspects and sentiments of the input reviews. This research also builds an Indonesian normalization dictionary to optimize the terms used to increase model accuracy. The test in aspect classification resulted in a precision value of 90%, recall of 88.73%, accuracy of 88.57%, and f1-score of 89%. Meanwhile, the sentiment classification resulted in a precision value of 96.15%, recall of 91.91%, accuracy of 94.28%, and f1-score of 93.98%. In addition, the test results (accuracy, f1-score, precision, recall) show that the lemmatization process is better than stemming and term weighting using the TF-IDF method is better than other methods (raw-term frequency, log-frequency weighting, binary-term weighting).https://ojs.unud.ac.id/index.php/lontar/article/view/105965 |
| spellingShingle | Dyah Ayu Wulandari Fitra A. Bachtiar Indriati Indriati Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine Lontar Komputer |
| title | Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine |
| title_full | Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine |
| title_fullStr | Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine |
| title_full_unstemmed | Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine |
| title_short | Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine |
| title_sort | aspect based sentiment analysis on shopee application reviews using support vector machine |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/105965 |
| work_keys_str_mv | AT dyahayuwulandari aspectbasedsentimentanalysisonshopeeapplicationreviewsusingsupportvectormachine AT fitraabachtiar aspectbasedsentimentanalysisonshopeeapplicationreviewsusingsupportvectormachine AT indriatiindriati aspectbasedsentimentanalysisonshopeeapplicationreviewsusingsupportvectormachine |