Analysis of Indonesia Politics Polarization before 2019 President Election Using Sentiment Analysis and Social Network Analysis

Full Text (PDF, 668KB), PP.22-30

Views: 0 Downloads: 0

Author(s)

Mohammad Nur Habibi 1,* Sunjana 1

1. Department of Informatics,Widyatama University Bandung, Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2019.11.04

Received: 13 Sep. 2019 / Revised: 26 Sep. 2019 / Accepted: 19 Oct. 2019 / Published: 8 Nov. 2019

Index Terms

Centrality values, classification, naïve bayes classifier, network attributes values, sentiment analysis, social network analysis

Abstract

The development of the Internet in Indonesia is quite rapid, it is marked by the increasing use of social networks, especially Twitter. Not only to share status or stories, Twitter has become become a means of promotion and campaign for elections. The Twitter data can be used to find out the political polarization in Indonesia that is needed in the 2019 presidential election. The method used in this research is sentiment analysis using naïve bayes classifier and social network analysis using the calculation of network attribute values and centrality values. 8.814 Twitter data was collected using data crawling method. The data are divided into three subsets consisting of jokowi’s sentiments, prabowo’s sentiments, and pilpres’s sentiments. Final result of the sentiment analysis is classified sentiments into positives, negatives, and neutral. The average value of the classification results was 91.27% positive sentiment, 7.56% negatives sentiment, and 1.17% neutral sentiment. This classification yielded the average accuracy of 69.2% for jokowi’s sentiments and 100% for prabowo sentiments. The classification accuracy calculation uses ROCs method. Final results of the social network analysis based on the calculation of network attributes yielded 277 nodes, 7.950 edges, 57,401 average degree, 56.44 average weighted degree, network diameter is 4, 1.853 average path length, 0.201 density, and 5 of number communities. Centrality values generates the 5 most influential actors in social network interactions are jokowi’s of first rank, 2nd SBYudhoyono’s, 3rd detikcom, 4th yjuniardi, 5th mohmahfudmd.

Cite This Paper

Mohammad Nur Habibi, Sunjana, " Analysis of Indonesia Politics Polarization before 2019 President Election Using Sentiment Analysis and Social Network Analysis ", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.11, pp. 22-30, 2019. DOI:10.5815/ijmecs.2019.11.04

Reference

[1]Asri Maspupah, Asep ID Hadiana. (2018). Pemetaan Bidang Keilmuan Organisasi Dengan Social Network Analysis. Universitas Jenderal Ahmad Yani, Bandung.
[2]Mahdi Shiddieqy, Dodie Tricahyono. (2017). Impementasi Social Network Analysis dalam Penyebaran Country Branding “Wonderful Indonesia”. Universitas Telkom, Bandung.
[3]Liu B, “sentiment analysis and subjectivity, Hanbook of natural language processing”, vol.2, pp 627-666, 2010
[4]S Padmajal and Prof. S. Sameen Fatima, “Opinion Mining and Sentiment Analysis -An assessment of People’s Belief: A Survey, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol. 4 No. 1, February 2013
[5]G. Fiarni, C., Maharani, H., & Pratama, R. (2016). Sentiment analysis system for Indonesia online retail shop review using hierarchy Naive Bayes technique. 2016 4th International Conference on Information and Communication Technology (ICoICT)
[6]Parveen, H., & Pandey, S. (2016). Sentiment analysis on Twitter Data-set using Naive Bayes algorithm. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)
[7]Yusainy Cleoputri, Anif Fatma Chawa, Siti Kholifah. (2017). Social Data Analytics sebagai Metode Alternatif dalam Riset Psikologi. Universitas Brawijaya, Malang.
[8]Hu Jie, Junchi Zhang, Mengchi Liu. (2014). A Semantic Model for Academic Social Network Analysis. Hubei University, Wuhan.
[9]Jaka Eka S, Erwin Budi S., Abdurahman Baizal (2018). Data Crawling pada Twitter. Telkom University, Bandung.
[10]Liu Guojun, Zhang Ming, Fei Yan. (2010). Large – Scale Social Network Analysis based on MapReduce. Peking University, Beijing.
[11]Akhtar Nadeem. (2014). Social Network Analysis Tools. Aligarh Muslim University. Aligarh
[12]Cheliotis, D. G. (2010). Social Network Analysis (SNA). National University of Singapore, Singapore.
[13]The Computer Advisor. Web site scraper the most effective tool for web data extraction, Retrieved November 28, 2018 from: http://www.thecomputeradvisor.net/web-site-scraper-the-most-effective-tool-for-web-data-extraction /
[14]Vatrapu Ravi, Raghava Rao, Abid Hussain. (2016). Social Set Analysis: A Set Theoretical Approach to Big Data. Copenhagen Business School, Computational Social Science Laboratory, Frederiksberg, Denmark
[15]Mujilahwati Siti. (2016). Pre-Processing Text Mining pada Data Twitter. Universitas Islam Lamongan, Lamongan.
[16]Rozi, Imam Fahrur; Pramono, Sholeh Hadi, Dahlan, Erfan Achmad. Implementasi Opinion Mining (Analisis Sentimen) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi. Jurnal EECCIS, [S.l.], v. 6, n. 1, p. pp. 37-43, mar. 2013. ISSN 2460-8122. Available at: <https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/164/142>. Date accessed: 26 Maret 2019
[17]Bo Pang, Lillian Lee, Shivakumar Vaithyanathan. Sentiment Classification Using Machine Learning Techniques.EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10 Pages 79-86.
[18]Rodiyansyah, Sandi Fajar; Winarko, Edi. Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), [S.l.], v. 6, n. 1, jan. 2013. ISSN 2460-7258. Available at: <https://jurnal.ugm.ac.id/ijccs/article/view/2144>. Date accessed: 26 Maret. 2019.
[19]Vinita Chandani, Romi Satria Wahono, Purwanto. 2015. Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film.Universitas Dian Nuswantoro, Semarang.
[20]Nur Azizah Vidya. 2015. Twitter Sentiment Analysis Terhadap Brand Reputation: Studi Kasus PT XL AXIATA Tbk. Universitas Indonesia, Jakarta.
[21]Jurafsky, D S. (2000). Speech and Language Processing "An Introduction to Natural Language Processing, Cmputationak Linguistics, and Specch Reconition. Prentice-Hall, Inc. New Jersey.
[22]M. Yusuf Nur Sumarno Putro. 2011 “Analisis Sentimen pada Dokumen berbahasa Indonesia dengan Pendekatan Support Vector Machine”. Masters, Binus.
[23]Rustiana, deden. 2017. Analisis sentiment pasar otomotif mobil: tweet twitter Jurnal simetris, vol 8. No 1. April 2017
[24]Novantirani A, Sabariah MK, Effendy V. 2004. Analisis Sentimen pada Twitter mengenai Penggunaan Transportasi Umum Darat dalam Kota dengan Metode Support Vector Machine. Universitas Telkom, Bandung.
[25]Putranti, Noviah Dwi, Winarko, Edi. Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), [S.l.], v. 8, n. 1, p. 91-100, jan. 2014. ISSN 2460-7258. Available at: <https://jurnal.ugm.ac.id/ijccs/article/view/3499>. Date accessed: 26 Maret. 2019.