Geo-Sentiment Analysis as a Location-Based Opinion Analysis System on Public Opinion Data about Governor Candidates

  • Authors

    • Imam Fahrur Rozi
    • Dika Rizky Yunianto
    • Mustika Mentari
    • Awan Setiawan
    • Rudy Ariyanto
    • Indrazno Siradjuddin
    2018-12-01
    https://doi.org/10.14419/ijet.v7i4.44.26873
  • Sentiment Analysis, Regional Election, Geosentiment, Naïve Bayes Classifier

  • Ahead of governor elections, there were a lot of news and opinions related to the candidates through social media. The candidates could map the positive public opinions as their political supports that need to be strengthened, and the negative opinions that need for correction. To map those opinions, it is necessary for an opinion classification system from textual opinions. It became the focus of this research. The system was designed to work on textual opinions in Bahasa since the proposed case study was the opinion of East Java governor candidates mainly written in Bahasa. Classification method that was used to classify the opinions in this system, is Naive Bayes Classifier (NBC). The opinions would be classified into 2 classes, negative and positive opinion. The classified opinions then grouped by region. It would make users easier to map the opinion in each region. The visualization became more user-friendly since the count of classified opinion displayed as a pie chart on a geographical mode or a map. After testing on the classification results, the accuracy value that we got was 78%. It indicated that NBC could perform very well as a simple text classification method with a good result.

     

  • References

    1. [1] CNN Indonesia. 2016. “Berita Teknologi Informasi : Twitter Rahasiakan Jumlah Pengguna di Indonesiaâ€. Diakses tanggal: 23 Maret 2018, Tersedia di: http://www.cnnindonesia.com/teknolog i/20160322085045-185-118939/twitterrahasiakan-jumlah-pengguna-diindonesia

      [2] Atmodjo. Juwono Tri, “Dinamika Partisipasi Politik Remaja melalui Media Sosial,†Jurnal Visi Komunikasi, vol. 13, no. 04, November 2014.

      [3] Bode. Leticia, Dalrymple. Kajsa E, “Politics in 140 Characters or Less: Campaign Communication, Network Interaction, and Political Participation on Twitter,†Journal of Political Marketing, vol. 15, issue 4, 2016.

      [4] Abdillah. Leon Adretti, “Social Media as Political Party Campaign in Indonesia,†Jurnal ilmiah Matrik, vol.16, no.1, April 2014.

      [5] Howard. Phipil N, “Deep Democracy, Thin Citizenship: The Impact of Digital Media in Political Campaign Strategy,†The ANNALS of the American Academy of Political and Social Science Journal, vol.597, issue 1, 2005.

      [6] Sarlan. Aliza, Nadam. Chayanit, Basri. Shuib, “Twitter Sentiment Analysis,†International Conference on Information Technology and Multimedia (ICIMU), Outrajaya, Malaysia, 2014.

      [7] P. Lai, “ExtractingStrongSentimentTrendfromTwitterâ€. Stanford University, 2012.

      [8] Sadida. Rizqon, et.al., “Perancangan Sistem Analisis Sentimen Masyarakat Pada Sosial Media Dan Portal Berita,†Seminar Nasional Teknologi Informasi dan Multimedia 2017, 2017.

      [9] Severyn. Aliaksei, Moschitti. Alessandro, “Twitter Sentiment Analysis with Deep Convolutional Neural Networksâ€. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015.

      [10] Ghiassi. Manoochehr, Zimbra. David, “Targeted Twitter Sentiment Analysis for Brands Using Supervised Feature Engineering and the Dynamic Architecture for Artificial Neural Networks,†Journal of Management Information Systems, vol.33, issue 4, 2016.

      [11] Perdana. R. S., Pinandito. A., “Combining Likes-Retweet Analysis and Naive Bayes Classifier Within Twitter for Sentiment Analysis,†International Conference On Communication And Computer Engineering (ICOCOE), Penang, Malaysia: Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 2017.

      [12] Rodiyansyah. F. S., Winarko. E., “Klasifi-kasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayes,†Indonesian Journal of Computing and Cybernetics Systems (IJCCS), 2013.

      [13] Hidayatullah. A., F., Azhari, “Analisis Sentimen Dan Klasifikasi Katrgori Terhadap Tokoh Publik Pada Twitter,†Seminar Nasional Informatika, Yogyakarta: UPN Veteran, 2014.

      [14] Rozi. I. F., Pramono. S. H., Dahlan. E. A., “Implementasi Opinion Mining (Analisis Sentimen) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi,†Jurnal EECCIS, vol.6, no.1, 2012.

      [15] Chin. D., Zappone. A., Zhao. J., “Analyzing Twitter Sentiment of the 2016 Presidential Candidates,†2016.

      [16] Jayanthi, Sreeja, “Sentiment Analysis of Twitter Data containing Emoticons: A Survey,†International Journal of Innovations in Engineering and Technology (IJIET), VI(4), 147- 153, 2016.

      [17] Dayalani. G. G., Patil. P. B., “Emoticon-based unsu-pervised sentiment classifier for polarity analysis in tweets,†International Journal of Engineering Research and General Sci-ence, II(6), 438-445, 2014.

      [18] Bansal, B., Srivastava. S., “On predicting elections with hybrid topic based sentiment analysis,†Procedia Computer Science 135, 346–353. 2018.

      [19] Kuše. E., Strembeck. M., “Politics, sentiments, and misinformation: An analysis of the Twitter,†Online Social Networks and Media 5, 37-50. 2018

      [20] Tellez. E. S., Miranda-Jim´enez. S., Graff. M., Moctezuma. D., Siordia. O. S., Villasenor. E. A., “A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis,†Expert Systems With Applications, doi: 10.1016/j.eswa.2017.03.071, 2017.

      [21] Pang. Bo, Lee. Lillian, “Opinion Mining and Sentiment Analysis," Now Publisher, 2008.

      [22] Nopyandri, “Pemilihan Kepala Daerah secara Langsung dalam Perspektif UUD 1945,†Inovatif Jurnal Ilmu Hukum, vol. 6, no. 7, 2013.

  • Downloads

  • How to Cite

    Fahrur Rozi, I., Rizky Yunianto, D., Mentari, M., Setiawan, A., Ariyanto, R., & Siradjuddin, I. (2018). Geo-Sentiment Analysis as a Location-Based Opinion Analysis System on Public Opinion Data about Governor Candidates. International Journal of Engineering & Technology, 7(4.44), 110-116. https://doi.org/10.14419/ijet.v7i4.44.26873