Aspect-based sentiment analysis on student reviews using the Indo-Bert base model
This study aims to gain a deeper understanding of online student reviews regarding the learning process at a private university in Indonesia and to compare the effectiveness of several algorithms: Naive Bayes, K-NN, Decision Tree, and Indo-Bert. Traditional Sentiment Analysis methods can only analyz...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/85/e3sconf_icenis2023_02004.pdf |
| _version_ | 1797345487565619200 |
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| author | Jazuli Ahmad Widowati Kusumaningrum Retno |
| author_facet | Jazuli Ahmad Widowati Kusumaningrum Retno |
| author_sort | Jazuli Ahmad |
| collection | DOAJ |
| description | This study aims to gain a deeper understanding of online student reviews regarding the learning process at a private university in Indonesia and to compare the effectiveness of several algorithms: Naive Bayes, K-NN, Decision Tree, and Indo-Bert. Traditional Sentiment Analysis methods can only analyze sentences as a whole, prompting this research to develop an Aspect-Based Sentiment Analysis (ABSA) approach, which includes aspect extraction and sentiment classification. However, ABSA has inconsistencies in aspect detection and sentiment classification. To address this, we propose the BERT method using the pre-trained Indo-Bert model, currently the best NLP model for the Indonesian language. This study also fine-tunes hyperparameters to optimize results. The dataset comprises 10,000 student reviews obtained from online questionnaires. Experimental results show that the aspect extraction model has an accuracy of 0.890 and an F1-Score of 0.897, while the sentiment classification model has an accuracy of 0.879 and an F1-Score of 0.882. These results demonstrate the effectiveness of the proposed method in identifying aspects and sentiments in student reviews and provide a comparison between the four algorithms. |
| first_indexed | 2024-03-08T11:18:19Z |
| format | Article |
| id | doaj.art-521949242d8744a6859b52c873a9e6b8 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T11:18:19Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-521949242d8744a6859b52c873a9e6b82024-01-26T10:28:00ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014480200410.1051/e3sconf/202344802004e3sconf_icenis2023_02004Aspect-based sentiment analysis on student reviews using the Indo-Bert base modelJazuli Ahmad0Widowati1Kusumaningrum Retno2Doctoral Program in Information Systems, School of Postgraduate Studies, Diponegoro UniversityDepartment of Mathematics, Faculty of Science and Mathematics, Diponegoro UniversityDepartment of Informatics, Faculty of Science and Mathematics, Diponegoro UniversityThis study aims to gain a deeper understanding of online student reviews regarding the learning process at a private university in Indonesia and to compare the effectiveness of several algorithms: Naive Bayes, K-NN, Decision Tree, and Indo-Bert. Traditional Sentiment Analysis methods can only analyze sentences as a whole, prompting this research to develop an Aspect-Based Sentiment Analysis (ABSA) approach, which includes aspect extraction and sentiment classification. However, ABSA has inconsistencies in aspect detection and sentiment classification. To address this, we propose the BERT method using the pre-trained Indo-Bert model, currently the best NLP model for the Indonesian language. This study also fine-tunes hyperparameters to optimize results. The dataset comprises 10,000 student reviews obtained from online questionnaires. Experimental results show that the aspect extraction model has an accuracy of 0.890 and an F1-Score of 0.897, while the sentiment classification model has an accuracy of 0.879 and an F1-Score of 0.882. These results demonstrate the effectiveness of the proposed method in identifying aspects and sentiments in student reviews and provide a comparison between the four algorithms.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/85/e3sconf_icenis2023_02004.pdfabsanaïve bayesk-nndecision treeindo-bert |
| spellingShingle | Jazuli Ahmad Widowati Kusumaningrum Retno Aspect-based sentiment analysis on student reviews using the Indo-Bert base model E3S Web of Conferences absa naïve bayes k-nn decision tree indo-bert |
| title | Aspect-based sentiment analysis on student reviews using the Indo-Bert base model |
| title_full | Aspect-based sentiment analysis on student reviews using the Indo-Bert base model |
| title_fullStr | Aspect-based sentiment analysis on student reviews using the Indo-Bert base model |
| title_full_unstemmed | Aspect-based sentiment analysis on student reviews using the Indo-Bert base model |
| title_short | Aspect-based sentiment analysis on student reviews using the Indo-Bert base model |
| title_sort | aspect based sentiment analysis on student reviews using the indo bert base model |
| topic | absa naïve bayes k-nn decision tree indo-bert |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/85/e3sconf_icenis2023_02004.pdf |
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