Multinomial Naïve Bayes for sentiment analysis of Indonesian's local government performance

Ahmad Azhari, Muhammad Saepul Hadi

Abstract


Digitalization of government performance, in conveying information and getting criticism, suggestions, and complaints from the public, is currently being carried out using social media. The use of social media is a form of government responsibility and openness to society. The high number of Twitter users in Indonesia, which reaches 6.43 million, allows the government to get many responses from the public. This background provides an opportunity for the public to be able to measure government performance based on a number of criticisms, suggestions, and complaints that the government responds to. However, public sentiment towards government performance has not been used as an evaluation and benchmark for the government in determining policies. The purpose of this research is to build a social media twitter sentiment analysis system to measure public sentiment towards local government performance by implementing Multinomial Naïve Bayes. This research is divided into several stages including tweet grabbing, manual tweet filtering, tweet labeling, split tweets, preprocessing tweets, term frequency, classification, and evaluation. The tweet retrieval process was carried out on 1 June - 31 July 2020 with 2000 tweets used from the total tweets obtained after manual filtering was carried out. This study shows that the sentiment analysis carried out obtained an accuracy of 80%, a precision of 78%, and a recall of 82%.

Keywords


Sentiment Analysis; Multinomial Naïve Bayes; Twitter; Local Government; Performance

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References


A. Aswari, “How digital technology driven millennial consumer behaviour in Indonesia,” Journal of Distribution Science, vol. 17, no. 8, pp. 25-34, 2019.

M. N. Aziz, A. Firmanto, A. M. Fajrin and R. V. Hari Ginardi, "Sentiment Analysis and Topic Modelling for Identification of Government Service Satisfaction," 2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 125-130, 2018.

P. P. Surya and B. Subbulakshmi, "Sentimental Analysis using Naive Bayes Classifier," 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), pp. 1-5, 2019.

A. Goel, J. Gautam and S. Kumar, "Real time sentiment analysis of tweets using Naive Bayes," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 257-261, 2016.

H. Hassani, C. Beneki, S. Unger, M. T. Mazinani, and M. R. Yeganegi, “Text mining in big data analytics,” Big Data and Cognitive Computing, vol. 4, no. 1, p. 1, 2020.

I. Kurniawati and H. F. Pardede, "Hybrid Method of Information Gain and Particle Swarm Optimization for Selection of Features of SVM-Based Sentiment Analysis," 2018 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 1-5, 2018.

M. M. Saritas, and A. Yasar, “Performance analysis of ANN and Naive Bayes classification algorithm for data classification,” International journal of intelligent systems and applications in engineering, vol. 7, no. 2, pp. 88-91, 2019.

K. Sarkar and M. Bhowmick, "Sentiment polarity detection in bengali tweets using multinomial Naïve Bayes and support vector machines," 2017 IEEE Calcutta Conference (CALCON), 2017, pp. 31-36, 2017.

D. Wang and R. Alfred, "A Review on Sentiment Analysis Model for Chinese Weibo Text," 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), pp. 456-463, 2020.

D. A. Kristiyanti, A. H. Umam, M. Wahyudi, R. Amin and L. Marlinda, "Comparison of SVM & Naïve Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Based on Public Opinion on Twitter," 2018 6th International Conference on Cyber and IT Service Management (CITSM), pp. 1-6, 2018.

E. Y. Sari, A. D. Wierfi and A. Setyanto, "Sentiment Analysis of Customer Satisfaction on Transportation Network Company Using Naive Bayes Classifier," 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM, pp. 1-6, 2019.

M. Wongkar and A. Angdresey, "Sentiment Analysis Using Naive Bayes Algorithm of The Data Crawler: Twitter," 2019 Fourth International Conference on Informatics and Computing (ICIC), pp. 1-5, 2019.

S. Das and A. K. Kolya, "Sense GST: Text mining & sentiment analysis of GST tweets by Naive Bayes algorithm," 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 239-244, 2017.

H. Parveen and S. Pandey, "Sentiment analysis on Twitter Data-set using Naive Bayes algorithm," 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 416-419, 2016.

S. P. Kristanto, J. A. Prasetyo and E. Pramana, "Naive Bayes Classifier on Twitter Sentiment Analysis BPJS of HEALTH," 2019 2nd International Conference of Computer and Informatics Engineering (IC2IE), pp. 24-28, 2019.




DOI: https://doi.org/10.31763/simple.v3i2.22

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Copyright (c) 2023 Ahmad Azhari, Muhammad Saepul Hadi

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Signal and Image Processing Letters
ISSN Online: 2714-6677 | Print: 2714-6669
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
Website : https://simple.ascee.org/index.php/simple/
Email 1 : simple@ascee.org
Email 2 : azhari@ascee.org


 

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