Study of Sentiment of Governor's Election Opinion in 2018

Authors

  • Agung Eddy Suryo Saputro  Department of Statistic, Bogor Agricultural University, Bogor, Indonesia
  • Khairil Anwar Notodiputro  Department of Statistic, Bogor Agricultural University, Bogor, Indonesia
  • Indahwati  Department of Statistic, Bogor Agricultural University, Bogor, Indonesia

DOI:

https://doi.org//10.32628/IJSRSET21841124

Keywords:

C5.0, Governor Election opinion, Naive Bayes, sentiment mining, Twitter.

Abstract

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

References

  1. Kwak, H., Lee, C., Park, H., and Moon, S.. 2010. "What is Twitter , a Social Network or a News Media??”. International World Wide Web Conference Com-mittee (IW3C2). pp. 591–600.
  2. Tumasjan, A., Sprenger, T.O., Sandner, P.G., and Welpe, I.M. 2010. "Predicting Elections with Twitter?: What 140 Characters Reveal about Political Sentiment”. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media Predicting. pp. 178–185.
  3. Hanna, A., Wells, C., Maurer, P., Friedland, L., Shah, D., and Matthes, J. 2013. "Partisan Alignments and Political Polarization Online?: A Computational Approach to Understanding the French and US Presidential Elections”. PLEAD’13.
  4. Indonesian Internet Service Providers Association. 2017. "Penetration & Behavior of Indonesian Internet Users".
  5. Ding, X., Liu, B., and Yu, P.S. 2008. "A Holistic Lexicon-Based Approach to Opinion Mining,” Proceeding of the 2008 International Conference on Web Search and Data Mining. pp. 231–240.
  6. Maks, I., and Vossen, P. 2012. "A lexicon model for deep sentiment analysis and opinion mining applications,” Decis. Support Syst. 53:680–688.
  7. Gentry, J. 2016. "Package " twitteR".
  8. Gentry, J, and Lang, D.T. 2015. "Package " ROAuth " .
  9. Fellows, I. 2018. "Package " wordcloud ".
  10. Grun, B., and Hornik, K. 2017. "Package " topicmodels ".
  11. Feinerer, I., and Hornik, K. 2018. "Package " tm ".
  12. Wickham, H. 2018. "Package " stringr " .
  13. Harrell, F.E.J. 2018. "Package " Hmisc ".
  14. Wei, T., Simko, V., Levy, M., Xie, Y., Jin, Y., and Zemla, J. 2017. "Package " corrplot ".
  15. Meyer, D., Dimitriadou, E., Hornik, K., Wingessel, A., Leisch, F., Chang, C.C., and Lin, C.C. 2018. "Package "e1071".
  16. Pang, B., and Lee, L. 2008. "Opinion Mining and Sentiment Analysis,” Inf. Retr. Boston., 2(1-2): 1–135.
  17. Manning, C.D., Raghavan, P., and Schutze, H. 2008. "Introduction to Information Retrieval". New York: Cambridge University Press.
  18. Alam, F., Mehmood, R., Katib, I., and Albeshri, A. 2016. "Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT),” Procedia Comput. Sci. 98:437–442.
  19. Han, J., Kamber, M., and Pei, J. 2012. "Data Mining – Concepts and Techniques Third Edition". Morgan Kaufmann Publishers.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2018-12-30

Issue

Volume 4, Issue 11, November-December-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
Agung Eddy Suryo Saputro, Khairil Anwar Notodiputro, Indahwati, " Study of Sentiment of Governor's Election Opinion in 2018, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 11, pp.231-238, November-December-2018. Available at doi : https://doi.org/10.32628/IJSRSET21841124