Identifying Emotion on Indonesian Tweets using Convolutional Neural Networks

  • Naufal Hilmiaji Telkom University
  • Kemas Muslim Lhaksmana Telkom University
  • Mahendra Dwifebri Purbolaksono Telkom University
Keywords: emotion, text classification, twitter, CNN

Abstract

especially with the advancement of deep learning methods for text classification. Despite some effort to identify emotion on Indonesian tweets, its performance evaluation results have not achieved acceptable numbers. To solve this problem, this paper implements a classification model using a convolutional neural network (CNN), which has demonstrated expected performance in text classification. To easily compare with the previous research, this classification is performed on the same dataset, which consists of 4,403 tweets in Indonesian that were labeled using five different emotion classes: anger, fear, joy, love, and sadness. The performance evaluation results achieve the precision, recall, and F1-score at respectively 90.1%, 90.3%, and 90.2%, while the highest accuracy achieves 89.8%. These results outperform previous research that classifies the same classification on the same dataset.

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Published
2021-06-26
How to Cite
Naufal Hilmiaji, Kemas Muslim Lhaksmana, & Mahendra Dwifebri Purbolaksono. (2021). Identifying Emotion on Indonesian Tweets using Convolutional Neural Networks. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 584 - 593. https://doi.org/10.29207/resti.v5i3.3137
Section
Artikel Teknologi Informasi