Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method
Based on Article 10 paragraph 1 of Law No. 14 of 2005, a teacher must have four competencies: pedagogical, personality, social, and professional. ICT training at Sunan Kalijaga State Islamic University involves instructors as educators who must have such competencies. An instructor's performanc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2021-01-01
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| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/61627 |
| _version_ | 1818321183878152192 |
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| author | Agung Pambudi Suprapto Suprapto |
| author_facet | Agung Pambudi Suprapto Suprapto |
| author_sort | Agung Pambudi |
| collection | DOAJ |
| description | Based on Article 10 paragraph 1 of Law No. 14 of 2005, a teacher must have four competencies: pedagogical, personality, social, and professional. ICT training at Sunan Kalijaga State Islamic University involves instructors as educators who must have such competencies. An instructor's performance is assessed through students' learning evaluation system by giving comments to the instructions. These comments contain positive and negative sentiments that can be reviewed by conducting sentiment analysis. Research related to sentiment analysis in recent years has been widely done, but researchers rarely pay attention to the effect of sentence length from the dataset on the method's performance. This study tried to analyze sentiment related to sentence length effect on ICT training student comments using Support Vector Machine and Convolutional Neural Network methods.
This study concluded that the sentence length on the dataset would affect the SVM and CNN methods' performance when combined with Word2vec. While the SVM+TFIDF method performance is not affected by sentence length, this method has the fastest process time than other methods. The CNN+Word2vec method produced the best performance in this study with a value of 0.94% accuracy, 0.95% precision, 0.96% recall, and 0.95% f1-score. |
| first_indexed | 2024-12-13T10:36:52Z |
| format | Article |
| id | doaj.art-2d16ce9e1cc940fc832825500d7000d1 |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-12-13T10:36:52Z |
| publishDate | 2021-01-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-2d16ce9e1cc940fc832825500d7000d12022-12-21T23:50:42ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582021-01-01151213010.22146/ijccs.6162729255Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network MethodAgung Pambudi0Suprapto Suprapto1Master Program in Computer Science, FMIPA UGM, YogyakartaDepartment of Computer Science and Electronics, FMIPA UGM, YogyakartaBased on Article 10 paragraph 1 of Law No. 14 of 2005, a teacher must have four competencies: pedagogical, personality, social, and professional. ICT training at Sunan Kalijaga State Islamic University involves instructors as educators who must have such competencies. An instructor's performance is assessed through students' learning evaluation system by giving comments to the instructions. These comments contain positive and negative sentiments that can be reviewed by conducting sentiment analysis. Research related to sentiment analysis in recent years has been widely done, but researchers rarely pay attention to the effect of sentence length from the dataset on the method's performance. This study tried to analyze sentiment related to sentence length effect on ICT training student comments using Support Vector Machine and Convolutional Neural Network methods. This study concluded that the sentence length on the dataset would affect the SVM and CNN methods' performance when combined with Word2vec. While the SVM+TFIDF method performance is not affected by sentence length, this method has the fastest process time than other methods. The CNN+Word2vec method produced the best performance in this study with a value of 0.94% accuracy, 0.95% precision, 0.96% recall, and 0.95% f1-score.https://jurnal.ugm.ac.id/ijccs/article/view/61627sentiment analysissvmcnn |
| spellingShingle | Agung Pambudi Suprapto Suprapto Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method IJCCS (Indonesian Journal of Computing and Cybernetics Systems) sentiment analysis svm cnn |
| title | Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method |
| title_full | Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method |
| title_fullStr | Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method |
| title_full_unstemmed | Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method |
| title_short | Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method |
| title_sort | effect of sentence length in sentiment analysis using support vector machine and convolutional neural network method |
| topic | sentiment analysis svm cnn |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/61627 |
| work_keys_str_mv | AT agungpambudi effectofsentencelengthinsentimentanalysisusingsupportvectormachineandconvolutionalneuralnetworkmethod AT supraptosuprapto effectofsentencelengthinsentimentanalysisusingsupportvectormachineandconvolutionalneuralnetworkmethod |