Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN

Herlina Jayadianti(1*); Wilis Kaswidjanti(2); Agung Tri Utomo(3); Shoffan Saifullah(4); Felix Andika Dwiyanto(5); Rafal Drezewski(6);

(1) Universitas Pembangunan Nasional Veteran Yogyakarta
(2) Universitas Pembangunan Nasional Veteran Yogyakarta
(3) Universitas Pembangunan Nasional Veteran Yogyakarta
(4) Universitas Pembangunan Nasional Veteran Yogyakarta, AGH University of Science and Technology
(5) AGH University of Science and Technology, Universitas Negeri Malang
(6) AGH University of Science and Technology
(*) Corresponding Author



Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.


Sentiment Analysis; IndoBERT; Recurrent Convolutional Neural Network; Pretained Language Models


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