Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM
The existence of KAI Access from PT. KAI prove their sincerity in serving consumers in this modern era. However, many negative reviews found in Google Play Store. There has been research on the review, but the analysis stage still at document level so the aspect related to the application is not kno...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Gadjah Mada
2022-04-01
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| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/68903 |
| _version_ | 1811238384631283712 |
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| author | Huda Mustakim Sigit Priyanta |
| author_facet | Huda Mustakim Sigit Priyanta |
| author_sort | Huda Mustakim |
| collection | DOAJ |
| description | The existence of KAI Access from PT. KAI prove their sincerity in serving consumers in this modern era. However, many negative reviews found in Google Play Store. There has been research on the review, but the analysis stage still at document level so the aspect related to the application is not known clearly and structured. So it is necessary to do an aspect-based sentiment analysis to extract the aspects and the sentiment. This study aims to do an aspect-based sentiment analysis on user reviews of KAI Access using Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), with 3 scenarios. Scenario 1 uses NBC with Multinomial Naive Bayes, scenario 2 uses SVM with default Sklearn library parameter, and scenario 3, uses SVM with hyperparameter tunning, while the data scrapped from Google Play Store. The results show the majority of user sentiment is negative for each aspect, with most discussed errors aspect shows the high system errors. The test results gives the best model from scenario 3 with an average accuracy 91.63%, f1-score 75.55%, precision 77.60%, and recall 74.47%. |
| first_indexed | 2024-04-12T12:41:56Z |
| format | Article |
| id | doaj.art-aa8142dd2782409c87d95ea254509aa9 |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-04-12T12:41:56Z |
| publishDate | 2022-04-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-aa8142dd2782409c87d95ea254509aa92022-12-22T03:32:46ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582022-04-0116211312410.22146/ijccs.6890331724Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVMHuda Mustakim0Sigit Priyanta1Undergraduate Program of Computer Science; FMIPA UGM, YogyakartaDepartment of Computer Science and Electronics, FMIPA UGM, YogyakartaThe existence of KAI Access from PT. KAI prove their sincerity in serving consumers in this modern era. However, many negative reviews found in Google Play Store. There has been research on the review, but the analysis stage still at document level so the aspect related to the application is not known clearly and structured. So it is necessary to do an aspect-based sentiment analysis to extract the aspects and the sentiment. This study aims to do an aspect-based sentiment analysis on user reviews of KAI Access using Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), with 3 scenarios. Scenario 1 uses NBC with Multinomial Naive Bayes, scenario 2 uses SVM with default Sklearn library parameter, and scenario 3, uses SVM with hyperparameter tunning, while the data scrapped from Google Play Store. The results show the majority of user sentiment is negative for each aspect, with most discussed errors aspect shows the high system errors. The test results gives the best model from scenario 3 with an average accuracy 91.63%, f1-score 75.55%, precision 77.60%, and recall 74.47%.https://jurnal.ugm.ac.id/ijccs/article/view/68903aspect-based sentiment analysiskai accesssupport vector machinenaive bayes classifier |
| spellingShingle | Huda Mustakim Sigit Priyanta Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM IJCCS (Indonesian Journal of Computing and Cybernetics Systems) aspect-based sentiment analysis kai access support vector machine naive bayes classifier |
| title | Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM |
| title_full | Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM |
| title_fullStr | Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM |
| title_full_unstemmed | Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM |
| title_short | Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM |
| title_sort | aspect based sentiment analysis of kai access reviews using nbc and svm |
| topic | aspect-based sentiment analysis kai access support vector machine naive bayes classifier |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/68903 |
| work_keys_str_mv | AT hudamustakim aspectbasedsentimentanalysisofkaiaccessreviewsusingnbcandsvm AT sigitpriyanta aspectbasedsentimentanalysisofkaiaccessreviewsusingnbcandsvm |