Analysis of Netizen Comments Sentiment on Public Official Statements on Instagram Social Media Accounts

Afwan Anggara; Suyud Widiono; Ahmad Tri Hidayat; Sutarman Sutarman

doi:10.25008/ijadis.v3i2.1244

Authors

Afwan Anggara Department of Information System, Universitas Teknologi Yogyakarta, Indonesia
Suyud Widiono Department of Computer Engineering, Universitas Teknologi Yogyakarta, Indonesia
Ahmad Tri Hidayat Department of Information System, Universitas Teknologi Yogyakarta, Indonesia
Sutarman Sutarman Magister Program of Information Technology, University of Technology Yogyakarta, Indonesia

DOI:

https://doi.org/10.25008/ijadis.v3i2.1244

Keywords:

Comment Classification, Public official, Instagram, Naive Bayes, Classifier, Sentiment Analysis

Abstract

Statements issued by public officials will be pros and cons in the community, there are those who respond positively, negatively or respond neutrally. Likewise on Instagram social media, every statement written on Instagram will get various responses written by netizens in the comment’s column posted. Netizen is an acronym for internet citizens, namely people who are actively using the internet. Due to the large number of comments, it is difficult to see whether the public response is a positive, negative or neutral comment when responding to statements from public officials. Whether the statements issued by public officials through Instagram have a positive, negative or neutral impact, so that if they can be grouped into labels, it can be seen how much public opinion is against these public figures. On social media accounts, not all comments written by netizens have the same writing structure, so we need a mechanism that is able to help analyze comments from netizens by classifying them into positive, negative or neutral response classes. By applying POS Tagging to determine opinion sentences or not and also the Naïve Bayes Classifier method and the tf-idf feature to be able to classify comments into several classes of positive, negative or neutral comments. The classification testing stage uses the cross validation method to test the accuracy of the naive bayes classification algorithm and the tf-idf feature.

Downloads

Download data is not yet available.

References

“Kamus Besar Bahasa Indonesia V Daring,” 2021. https://kbbi.kemdikbud.go.id/entri/netizen (accessed Nov. 25, 2021).

Saputra, N., Adji, T.B. dan Permanasari, A.E., Analisis Sentimen Data Presiden Jokowi dengan Preprocessing Normalisasi dan Stemming menggunakan Metode Naïve Bayes dan SVM, Jurnal Dinamika Informatika, Vol. 5, No. 1, pp. 43-54, 2015.

Gusriani, S., Wardhani, K.D.K. dan Zul, M.I., Analisis Sentimen Terhadap Toko Online di Sosial Media Menggunakan Metode Klas-ifikasi Naïve Bayes (Studi Kasus: Facebook Page BerryBenka), 4th Applied Business and Engineering Conference, Politeknik Caltex Riau, Riau, September 2016.

Falahah. dan Nur, D.D.A., 2015, Pengembangan Aplikasi Sentiment Analysis menggunakan Metode Naïve Bayes (Studi Kasus Senti-ment Analysis dari Media Twitter), Seminar Nasional Sistem Informasi Indonesia, Institut Teknologi Sepuluh Nopember, Surabaya, November 2015.

Rish, I., An Empirical Study of The Naive Bayes Classifier, International Joint Conference on Artificial Intelligence, California, pp. 41-46, 2006.

Liu, B., Sentiment Analysis and Opinion Mining, Morgan & Claypool Publisher, USA, 2012.

Rozi, I.F., Pramono, S.H. dan Dahlan, E.A., Implementasi Opinion Mining (Analisis Sentimen) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi, Jurnal EECCIS, Vol. 6, No. 1, pp. 37-43, 2012.

Sebastian, F., Machine Learning in Automated Text Categorization, ACM Computing Surveys, Vol. 34, pp. 1-47, 2002.

Go, A., Bhayani, R. and Huang, L., Twitter Sentiment Classification using Distant Supervision, CS224N Project Report, Stanford, 2009.

Manning, C. D., Raghavan, P. And Schütze, H., Introduction to Information Retrieval, Cambridge University Press, Cambridge, 2008.

Wang, Y., Wang, S., Tang, J., Liu, H. and Li, Baoxin., Unsupervised Sentiment Analysis for Social Media Images, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), Arizona State University Tempe, Arizona, Jan-uary 2015.