Sentiment Analysis to Prediction DKI Jakarta Governor 2017 on Indonesian Twitter

Ghulam Asrofi Buntoro

Abstract

This study was conducted to test opinion data tweet of three candidates for governor Jakarta, 2017. Data only in Indonesian tweet, data tweet 100 tweets with keywords AHY, 100 tweets with keywords Ahok, and 100 tweets with keywords Anies. Data taken by random either from a normal user or online media at Twitter. Indonesian tweet opinion with three candidates for governor Jakarta in 2017 divided into three sentiment: positive, neutral and negative. Preprocessing data is, Lower Case Tokens, Normalization, Tokenization, Cleansing and Filtering. Classification method in this study using Naïve Bayes classifier (NBC), because this method is the most widely performed for sentiment analysis and proven always produce highest accuracy. Results of classification, Precision AHY data scored the highest with 95% and 95% Recall, while Ahok data lowest Precision scores with 81.6% and 82% recall.

Full Text:

PDF

References

KPUD DKI Jakarta (2016) Agenda Pemilihan Gubernur DKI Jakarta http://kpujakarta.go.id/agenda/

Top Media Sosial http://www.evadollzz.com/2014/09/top-10-social-networkings-terpopuler.html

Marian Radke Yarrow, John A. Clausen and Paul R. Robbins (2010). The Social Meaning of Mental Illness. Journal of Social Issues. Volume 11, Issue 4, pages 33–48, Fall 1955.

Go, A., Huang, L., & Bhayani, R. (2009). Twitter Sentiment Analysis. Final Project Report, Stanford University, Department of Computer Science.

Mahyuddin K. M. Nasution. Social Network Mining (SNM): A Definition of Relation between The Resources and SNA. International Journal on Advanced Science, Engineering and Information Technology. Vol.6 (2016) No. 6, ISSN: 2088-5334

Suhaila Zainudin, Dalia Sami Jasim, and Azuraliza Abu Bakar. Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction. International Journal on Advanced Science, Engineering and Information Technology. Vol.6 (2016) No. 6, ISSN: 2088-5334

Merfat M. Altawaier, Sabrina Tiun. Comparison of Machine Learning Approaches on Arabic Twitter Sentiment Analysis. International Journal on Advanced Science, Engineering and Information Technology. Vol.6 (2016) No. 6, ISSN: 2088-5334

Mesut Kaya, Guven Fidan, Ismail H. Toroslu (2012). Sentiment Analysis of Turkish Political News. IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

Pak, A., dan Paurobek, P., (2010). Twitter as a Corpus for Sentiment Analysis and Opinion Mining, Universite de Paris-Sud, Laboratoire LIMSI-CNRS.

G. A. Buntoro, (2016). " Sentiment Analysis Candidates of Indonesian Presiden 2014 with Five Class Attribute" in International Journal of Computer Applications (0975 – 8887), Volume 136 – No.2, February 2016.

Franky dan Manurung, R., (2008). Machine Learning-based Sentiment Analysis of Automatic Indonesia n Translations of English Movie Reviews. In Proceedings of the International Conference on Advanced Computational Intelligence and Its Applications.

ARFF files from Text Collections. http://WEKA.wikispaces.com/ARFF+files+from+Text+Collections.

Tala, F. Z. (2003). A Study of Stemming Effects on Information Retrieval in Bahasa Indonesia. M.S. thesis. M.Sc. Thesis. Master of Logic Project. Institute for Logic, language and Computation. Universiteti van Amsterdam The Netherlands.

Class StringToWordVector. http://WEKA.sourceforge.net/doc.de.v/WEKA/filters/unsupervised/attribute/StringToWordVector.html.

Ian H. Witten. (2013) Data Mining with WEKA. Department of Computer Science University of Waikato New Zealand.

Kohavi, & Provost. (1998) Confusion Matrix http://www2.cs.uregina.ca/~dbd/cs831/notes/confusion_matrix/confusion_matrix.html

DOI

https://doi.org/10.21107/ijseit.v2i1.2744

Metrics

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 International Journal of Science, Engineering, and Information Technology

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