Sentiment Analysis Research in Indonesian Language Reviewing From the Characteristics of Comments

M. Isnin Faried, Lely Priska D. Tampubolon, Dwi Atmodjo


This study focuses on identifying the method of sentiment analysis using the data sources from different social media. The comments are classified based on the themes: politics, business product reviews, events, etc. The study also focuses on the form of the language and the data types: text only and texts with emoticons. The literature review is conducted on several previous studies with characteristics: Indonesian language, sarcasm, the method used, and the development of certain features in the sentiment analysis method. There are three conclusions. First, there are polite comments and impolite/ sarcastic comments. Comments posted on government channels are more polite than those on social media channels. All the comments have the same polarity. The comments use words only or a combination of words and emoticons. The analysis becomes complex because the comments use slang words. Second, the support vector machine (SVM) method is widely used. The use of libraries in doing sentiment analysis in Indonesian is helpful, but only a few are suitable for general purposes. Finally, the feature development has many variants which can be customized based on the needs, and SentiWordNet is the most popular supporting application.


Comments, Dataset, Feature Development, Sentiment Analysis

Full Text:



U. Hemamalini and S. Perumal, “Literature review on sentiment analysis,” Int. J. Sci. Technol. Res., vol. 9, no. 4, pp. 2009–2013, 2020.

C. Bhadane, H. Dalal, and H. Doshi, “Sentiment Analysis: Measuring Opinions,” in Procedia Computer Science, 2015, vol. 45, pp. 808–814.

W. Medhat, A. Hassan, and H. Korashy, “Sentiment Analysis Algorithms and Applications: A Survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, 2014.

P. Nomleni, M. Hariadi, and I. K. E. Purnama, “Sentiment Analysis Berbasis Big Data,” in Seminar Nasional Rekayasa Teknologi Industri dan Informasi, 2014, vol. 9, pp. 142–149.

B. Kitchenham and S. Charters, “Guidelines for Performing Systematic Literature Reviews in Software Engineering,” 2007.

R. S. Wahono, “A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks,” J. Softw. Eng., vol. 1, no. 1, pp. 1–16, 2011.

S. Christina and D. Ronaldo, “A Survey of Sentiment Analysis Using Sentiwordnet on Bahasa Indonesia,” J. Teknol. Inf., vol. 12, no. 2, pp. 169–174, 2018.

R. Ferdiana, F. Jatmiko, D. D. Purwanti, A. S. T. Ayu, and W. F. Dicka, “Dataset Indonesia untuk Analisis Sentimen,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 4, p. 334, 2019.

E. Lunando and A. Purwarianti, “Indonesian Social Media Sentiment Analysis with Sarcasm Detection,” 2013 Int. Conf. Adv. Comput. Sci. Inf. Syst. ICACSIS 2013, pp. 195–198, 2013.

R. Habibi, D. B. Setyohadi, and E. Wati, “Analisis Sentimen Pada Twitter Mahasiswa Menggunakan Metode Backpropagation,” J. Inform., vol. 12, no. 1, pp. 103–109, 2016.

H. Juwiantho et al., “Sentiment Analysis Twitter Bahasa Indonesia Berbasis WORD2VEC Menggunakan Deep Convolutional Neural Network,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 1, pp. 181–188, 2020.

N. Monarizqa, L. E. Nugroho, and B. S. Hantono, “Penerapan Analisis Sentimen Pada Twitter Berbahasa Indonesia Sebagai Pemberi Rating,” J. Penelit. Tek. Elektro dan Teknol. Inf., vol. 1, no. 3, pp. 151–155, 2014.

L. Septiani and Y. Sibaroni, “Sentiment Analysis Terhadap Tweet Bernada Sarkasme Berbahasa Indonesia,” J. Linguist. Komputasional, vol. 2, no. 2, pp. 62–67, 2019.

R. Kumar, D. Sarddar, I. Sarkar, R. Bose, and S. Roy, “A Literature Survey On Sentiment Analysis Techniques Involving Social Media And Online Platforms,” Int. J. Sci. Technol. Res., vol. 9, no. 05, p. 5, 2020.

F. Septianingrum and A. S. Y. Irawan, “Metode Seleksi Fitur Untuk Klasifikasi Sentimen Menggunakan Algoritma Naive Bayes: Sebuah Literature Review,” J. Media Inform. Budidarma, vol. 5, no. 3, p. 799, 2021.



  • There are currently no refbacks.

Copyright (c) 2022 Lely Priska Dameria

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.