Optimization of Sentiment Analysis for Indonesian Presidential Election using Naïve Bayes and Particle Swarm Optimization

Nur Hayatin; Gita Indah Marthasari; Lia Nuarini

doi:10.15575/join.v5i1.558

Authors

Nur Hayatin Universitas Muhammadiyah Malang, Indonesia
Gita Indah Marthasari Universitas Muhammadiyah Malang, Indonesia
Lia Nuarini Universitas Muhammadiyah Malang, Indonesia

DOI:

https://doi.org/10.15575/join.v5i1.558

Keywords:

NaÃ¯ve Bayes, Opinion extraction, Sentiment analysis, Particle swarm optimization

Abstract

Twitter can be used to analyze sentiment to get public opinion about public figures to find a trend in positive or negative responses, especially to analyze sentiments related to presidential candidates in the 2019 election in Indonesia. Naïve Bayes (NB) can be used to classify tweet feed into polarity class negative or positive, but it still has low accuracy. Therefore, this study optimizes the Naïve Bayes algorithm with Particle Swarm Optimization (NB-PSO) to classify opinions from twitter feeds to get a good accuracy of public figures sentiment analysis. PSO used to select features to find optimization values to improve the accuracy of Naïve Bayes. There are four steps to optimize NB using PSO, i.e., initializing the population (swarm), calculate the accuracy value that matched with selected features, selected the best accuracy of classification, and updating position and velocity. From this study, the group of tweets was obtained based on the positive and negative sentiments from the community towards two Indonesia presidential candidates in 2019. The NB-PSO test shows the accuracy result of 90.74%. The result of accuracy increases by 4.12% of the NB algorithm. In conclusion, the inclusion of the Particle Swarm Optimization algorithm for Naïve Bayes classification algorithm gives a significant accuracy, especially for sentiment analysis cases.

References

R. Rezapour, L. Wang, and J. Diesner, “Identifying the Overlap between Election Result and Candidates ’ Ranking based on Hashtag-Enhanced, Lexicon-Based Sentiment Analysis,” 2017.

R. D. Desai, “Sentiment Analysis of Twitter Data,” in 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), 2018.

S. Mukherjee and P. K. Bala, “Detecting sarcasm in customer tweets: an NLP based approach.,” Ind. Manag. Data Syst., vol. 117, no. 6, 2017.

S. Rana and A. Singh, “Comparative Analysis of Sentiment Orientation Using SVM and Naïve Bayes Techniques,” in 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), 2016, no. October, pp. 106–111.

S. Ernawati, “Penerapan Particle Swarm Optimization untuk Seleksi Fitur pada Analisis Sentimen Review Perusahaan Penjualan Online Menggunakan Naïve Bayes,” 2016.

A. Nurhadi, “Implementasi Algoritma Naïve Bayes Classifier Berbasis Particle Swarm Optimization (PSO) untuk Klasifikasi Konten Berita Digital Bahasa Indonesia,” 2016.

K. A.?; T. Kumar, “Email Spam Detection Using Integrated Approach of Naïve Bayes and Particle Swarm Optimization,” in 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS).

A. P. W.?; H. A. Santoso, “Improving The Accuracy of Naïve Bayes Algorithm for Hoax Classification Using Particle Swarm Optimization,” in 2018 International Seminar on Application for Technology of Information and Communication, 2018.

A. Idrus, “Sentiment Analysis Of State Officials News On Online Media Based On Public Opinion Using Naive Bayes Classifier Algorithm And Particle Swarm Optimization,” 2018 6th Int. Conf. Cyber IT Serv. Manag., no. Citsm, pp. 1–7, 2018.

L. H. Nguyen and A. Salopek, “A Natural Language Normalization Approach to Enhance Social Media Text Reasoning,” in In 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 2019–2026.

A. Rahmatulloh, N. I. Kurniati, I. Darmawan, A. Z. Asyikin, and D. W. J, “Comparison between the Stemmer Porter Effect and Nazief-Adriani on the Performance of Winnowing Algorithms for Measuring Plagiarism,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. August, pp. 1124–1128, 2019.

C. Fiarni, “Sentiment Analysis System for Indonesia Online Retail Shop Review Using Hierarchy Naive Bayes Technique,” in 2016 4th international conference on information and communication technology (ICoICT), 2018, no. May 2016, pp. 1–6.

J. O. Akella and L. N. Y. Akella, “Sentiment Analysis Using Naïve Bayes Algorithm,” in 2018 3rd International Conference on Inventive Computation Technologies (ICICT), 2018, pp. 1–4.

A. Jeyaraj and M. J. Nithya, “Comparison of Feature Selection Strategies for Classification using Rapid Miner,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 4, no. January, pp. 13556–13563, 2018.

T. Puyalnithi, M. Viswanatham, and A. Singh, “Comparison of Performance of Various Data Classification Algorithms with Ensemble Methods Using RAPIDMINER,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 5, no. April, pp. 1–6, 2016.