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


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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Statcounter, "Statcounter GlobalStats," Statcounter, March 2021. [Online]. Available: [Accessed 22 April 2021].

M. Zaskya, A. Boham and L. J. H. Lotulung, "Twitter Sebagai Media Mengungkapkan Diri Pada Kalangan Milenial," ACTA DIURNA KOMUNIKASI, vol. 3, 2021.

A. Mehrabian, Nonverbal communication, Routledge, 2017.

N. Y. Hutama, K. M. Lhaksmana and I. Kurniawan, "Text Analysis of Applicants for Personality Classification Using Multinomial Naı̈ve Bayes and Decision Tree," JURNAL INFOTEL, vol. 12, p. 72–81, 2020.

J. K. Rout, K.-K. R. Choo, A. K. Dash, S. Bakshi, S. K. Jena and K. L. Williams, "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, vol. 18, p. 181–199, 2018.

J. W. Simanullang, A. Adiwijaya and S. Al Faraby, "Klasifikasi Sentimen Pada Movie Review Dengan Metode Multinomial Naı̈ve Bayes," eProceedings of Engineering, vol. 4, 2017.

M. Yulietha, S. A. Faraby, W. C. Widyaningtyas and others, "An implementation of support vector machine on sentiment classification of movie reviews," in Journal of Physics: Conference Series, 2018.

Y. Chen and Z. Zhang, "Research on text sentiment analysis based on CNNs and SVM," in 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018.

H. Kim and Y.-S. Jeong, "Sentiment classification using convolutional neural networks," Applied Sciences, vol. 9, p. 2347, 2019.

M. S. Saputri, R. Mahendra and M. Adriani, "Emotion classification on indonesian twitter dataset," in 2018 International Conference on Asian Language Processing (IALP), 2018.

A. Larasati, B. Harijanto and F. Rahutomo, "Implementasi Deep Learning Untuk Deteksi Ekspresi Emosi Pada Twitter," in Seminar Informatika Aplikatif Polinema, 2020.

H. A. Robbani, "PySastrawi," 28 September 2018. [Online]. Available:

F. Chollet and others, Keras, 2015.

M. Abadi and others, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015.

Goodfellow, Y. Bengio, A. Courville and Y. Bengio, Deep learning, vol. 1, MIT press Cambridge, 2016.

Y. Li and T. Yang, "Word embedding for understanding natural language: a survey," in Guide to big data applications, Springer, 2018, p. 83–104.

A. Zell, N. Mache, R. Hübner, G. Mamier, M. Vogt, M. Schmalzl and K.-U. Herrmann, "SNNS (stuttgart neural network simulator)," in Neural network simulation environments, Springer, 1994, p. 165–186.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," The journal of machine learning research, vol. 15, p. 1929–1958, 2014.

Y. Bengio, R. Ducharme, P. Vincent and C. Janvin, "A neural probabilistic language model," The journal of machine learning research, vol. 3, p. 1137–1155, 2003.

M. Hossin, M. N. Sulaiman, A. Mustapha, N. Mustapha and R. W. Rahmat, "A hybrid evaluation metric for optimizing classifier," in 2011 3rd Conference on Data Mining and Optimization (DMO), 2011.

Y. Sasaki, "The truth of the F-measure," Teach Tutor Mater, 1 2007.

D. Jurafsky and J. Martin, "Naive bayes and sentiment classification," Speech and language processing, p. 74–91, 2017.

X. Zhang, J. Zhao and Y. LeCun, "Character-level convolutional networks for text classification," arXiv preprint arXiv:1509.01626, 2015.

S. Bai, J. Z. Kolter and V. Koltun, "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling," arXiv preprint arXiv:1803.01271, 2018.

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.
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