An automatic lexicon generation for indonesian news sentiment analysis: a case on governor elections in Indonesia

Media A Ayu, Sony Surya Wijaya, Teddy Mantoro


Sentiment analysis has been popularly used in analyzing data from the internet.  One of the techniques used is lexicon based sentiment analysis.  Generating lexicon is not an easy process, and lexicon in Bahasa Indonesia is rarely available.  This paper proposes an automatic lexicon generation in Bahasa Indonesia for sentiment analysis purpose.  Experiments were performed using the generated lexicon for doing sentiment analysis on Indonesian political news about the 2018 governor election in three provinces in Indonesia. The conducted experiments show promising results where it can predict the candidate’s rank, the election winner, and the percentage of votes for each candidate with better accuracy than the previous work which used manually generated lexicon.


lexicon generation; sentiment analysis; Indonesian lexicon; news sentiment analysis; automatic lexicon generation

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Alexa. (2018). Top Sites in Indonesia. Retrieved from countries/ID

Montoyo, A., Martínez-barco, P., & Balahur, A. (2012). Subjectivity and sentiment anal-ysis : An overview of the current state of the area and envisaged developments. Decision Support Systems, 53(4), 675–679.

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval, Vol. 2(1).

Mada, U. G., Nurwidyantoro, A., & Mada, U. G. (2016). Sentiment Analysis of Eco-nomic News in Bahasa Indonesia Using Majority Vote Classifier.

Troussas, C., Virvou, M., Espinosa, K. J., Llaguno, K., & Caro, J. (2013). Sentiment analysis of Facebook statuses using Naive Bayes Classifier for language learning. 4th International Conference on Information, Intelligence, Systems and Applications (IISA 2013), 198–205.

Ortigosa, A., Martín, J. M., & Carro, R. M. (n.d.). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31(1), 527–541.

Öztürk, N., & Ayvaz, S. (2017). Sentiment Analysis on Twitter : A Text Mining Ap-proach to the Syrian Refugee Crisis. Telematics and Informatics, (October).

Soroinda, A. A. R., Rachim, F., & Wonggo, M. I. (2016). A Corpus-Based Lexicon Building in Indonesian Political Context Through Indonesian Online News, 347–352.

Fast, E., Chen, B., & Bernstein, M. (2016). Empath: Understanding Topic Signals in Large-Scale Text. Proceedings of the 2016 CHI Conference on Human Factors in Com-puting Systems (CHI 2016), pages 4647-4657.

Khoo, C. S. G., & Johnkhan, S. B. (2018). Lexicon-based sentiment analysis: Compara-tive evaluation of six sentiment lexicons. Journal of Information Science, 44(4), 491–511.

Devika, M. D., Sunitha, C., & Ganesh, A. (2016). Sentiment Analysis: A Comparative Study on Different Approaches. Procedia Computer Science, 87, 44–49.

Melville, P., Gryc, W., & Lawrence, R. D. (2009). Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1275–1284). New York, NY, USA: ACM.

Hailong, Z., Wenyan, G., Bo, J. (2014). Machine Learning and Lexicon Based Me-thods for Sentiments Classification: A Survey. Proceedings of 11th Web Information System and Application Conference (WISA 2014), pp. 262-265.

Alpaydin, E. (2010). Introduction to Machine Learning (2nd ed.). The MIT Press.

Sharma, A., & Dey, S. (2012). An Artificial Neural Network Based Approach for Sen-timent Analysis of Opinionated Text. In Proceedings of the 2012 ACM Research in Applied Computation Symposium (pp. 37–42). New York, NY, USA: ACM.

Zhang, Z., Ye, Q., Zhang, Z., & Li, Y. (2011). Sentiment classification of Internet res-taurant reviews written in Cantonese. Expert Systems with Applications, 38(6), 7674–7682.

Pang, B.,Lee, L., and Vaithyanathan,S. (2002), Thumbsup?:sentiment classification us-ing machine learning techniques, Proceedings of the Conference on Empirical Methods in Natural Language Processing—Volume10 (EMNLP’02), pp.79–86,Association for Computational Linguistics, Stroudsburg, Pa, USA, July2002

Li, W., Liu, P., Zhang, Q., Liu, W. (2019). An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism. Future Internet, Vol. 11 issue 4,

Li, G., & Liu, F. (2012). Application of a clustering method on sentiment analysis. Jour-nal of Information Science, 38(2), 127–139.

Ekman, P. (1992), “An Argument for Basic Emotions,” Cognition e-Emotion, Vol.6 No.3-4, pp 169-200.

Davis, J., & Goadrich, M. (2006). The Relationship between Precision-Recall and ROC Curves. In Proceedings of the 23rd International Conference on Machine Learning (pp. 233–240). New York, NY, USA: ACM.

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