SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis
Twitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and...
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Department of Informatics, UIN Sunan Gunung Djati Bandung
2024-05-01
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| author | Annisa Mufidah Sopian Ridwan Ilyas Fatan Kasyidi Asep Id Hadiana |
| author_facet | Annisa Mufidah Sopian Ridwan Ilyas Fatan Kasyidi Asep Id Hadiana |
| author_sort | Annisa Mufidah Sopian |
| collection | DOAJ |
| description | Twitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and Rule prediction, that used language rule-based approach using syntactic features for aspect and opinion extraction, and we compare several algorithm for rule prediction such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN), Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) that can generate rules with a tree-based approach. By employing syntactic features and rule prediction, it has been able to explore important features in a sentence. In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. While XGBoost was identified as the most effective model for opinions rule prediction, with MSE of 0.013, RMSE of 0.117, and MAE of 0.075. Since we used syntactic feature-based approaches and rule prediction in this work, it is expected to be implemented for other cases, with other domain datasets. |
| format | Article |
| id | doaj-art-e0039ca5bb044c7cb1bd222e067d79af |
| institution | Kabale University |
| issn | 2528-1682 2527-9165 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Department of Informatics, UIN Sunan Gunung Djati Bandung |
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| series | JOIN: Jurnal Online Informatika |
| spelling | doaj-art-e0039ca5bb044c7cb1bd222e067d79af2025-08-20T03:25:55ZengDepartment of Informatics, UIN Sunan Gunung Djati BandungJOIN: Jurnal Online Informatika2528-16822527-91652024-05-019110011010.15575/join.v9i1.12751345SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment AnalysisAnnisa Mufidah Sopian0Ridwan Ilyas1Fatan Kasyidi2Asep Id Hadiana33Department of Informatics, Faculty of Science and Informatics, Universitas Jenderal Achmad Yani3Department of Informatics, Faculty of Science and Informatics, Universitas Jenderal Achmad Yani3Department of Informatics, Faculty of Science and Informatics, Universitas Jenderal Achmad YaniFaculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, MelakaTwitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and Rule prediction, that used language rule-based approach using syntactic features for aspect and opinion extraction, and we compare several algorithm for rule prediction such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN), Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) that can generate rules with a tree-based approach. By employing syntactic features and rule prediction, it has been able to explore important features in a sentence. In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. While XGBoost was identified as the most effective model for opinions rule prediction, with MSE of 0.013, RMSE of 0.117, and MAE of 0.075. Since we used syntactic feature-based approaches and rule prediction in this work, it is expected to be implemented for other cases, with other domain datasets.https://join.if.uinsgd.ac.id/index.php/join/article/view/1275taxationaspect-opinion extractionrule prediction saersyntactic feature |
| spellingShingle | Annisa Mufidah Sopian Ridwan Ilyas Fatan Kasyidi Asep Id Hadiana SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis JOIN: Jurnal Online Informatika taxation aspect-opinion extraction rule prediction saer syntactic feature |
| title | SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis |
| title_full | SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis |
| title_fullStr | SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis |
| title_full_unstemmed | SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis |
| title_short | SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis |
| title_sort | saer comparison of rule prediction algorithms on constructing a corpus for taxation related tweet aspect based sentiment analysis |
| topic | taxation aspect-opinion extraction rule prediction saer syntactic feature |
| url | https://join.if.uinsgd.ac.id/index.php/join/article/view/1275 |
| work_keys_str_mv | AT annisamufidahsopian saercomparisonofrulepredictionalgorithmsonconstructingacorpusfortaxationrelatedtweetaspectbasedsentimentanalysis AT ridwanilyas saercomparisonofrulepredictionalgorithmsonconstructingacorpusfortaxationrelatedtweetaspectbasedsentimentanalysis AT fatankasyidi saercomparisonofrulepredictionalgorithmsonconstructingacorpusfortaxationrelatedtweetaspectbasedsentimentanalysis AT asepidhadiana saercomparisonofrulepredictionalgorithmsonconstructingacorpusfortaxationrelatedtweetaspectbasedsentimentanalysis |