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All Journal International Journal of Electrical and Computer Engineering Harmonia: Journal of Research and Education JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Journal of Applied Data Sciences Bulletin of Social Informatics Theory and Application Advance Sustainable Science, Engineering and Technology (ASSET)
Nafalski, Andrew
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Arts Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Social Sciences

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Journal : International Journal of Electrical and Computer Engineering

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Detecting emotions using a combination of bidirectional encoder representations from transformers embedding and bidirectional long short-term memory Wibawa, Aji Prasetya; Cahyani, Denis Eka; Prasetya, Didik Dwi; Gumilar, Langlang; Nafalski, Andrew
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7137-7146

Abstract

One of the most difficult topics in natural language understanding (NLU) is emotion detection in text because human emotions are difficult to understand without knowing facial expressions. Because the structure of Indonesian differs from other languages, this study focuses on emotion detection in Indonesian text. The nine experimental scenarios of this study incorporate word embedding (bidirectional encoder representations from transformers (BERT), Word2Vec, and GloVe) and emotion detection models (bidirectional long short-term memory (BiLSTM), LSTM, and convolutional neural network (CNN)). With values of 88.28%, 88.42%, and 89.20% for Commuter Line, Transjakarta, and Commuter Line+Transjakarta, respectively, BERT-BiLSTM generates the highest accuracy on the data. In general, BiLSTM produces the highest accuracy, followed by LSTM, and finally CNN. When it came to word embedding, BERT embedding outperformed Word2Vec and GloVe. In addition, the BERT-BiLSTM model generates the highest precision, recall, and F1-measure values in each data scenario when compared to other models. According to the results of this study, BERT-BiLSTM can enhance the performance of the classification model when compared to previous studies that only used BERT or BiLSTM for emotion detection in Indonesian texts.
Co-Authors Aji Prasetya Wibawa Andini, Nurul Fajriah Anusua Ghosh, Anusua Atmaja, Nimas Hadi Darwis, Herdianti Denis Eka Cahyani Dewandra, Aderyan Reynaldi Fahrezza Dewi, Popy Maulida Didik Dwi Prasetya Dwiyanto, Felix Andika Erna Daniati Hariyono Hariyono Haviluddin Haviluddin Hery Widijanto Izdihar, Zahra Nabila Langlang Gumilar Marida, Tyas Agung Cahyaning Muladi Nedic, Zorica Ningtyas, Yana Nurfadila, Piska Dwi Purnawansyah Purnawansyah Putra Utama, Agung Bella Raja, Roesman Ridwan Saputra, Irzan Tri Triyanna Widiyaningtyas Utama, Agung Bella Putra Utomo Pujianto Wahyu Sakti Gunawan Irianto Zaeni, Ilham Ari Elbaith