A benchmark of modeling for sentiment analysis of the Indonesian Presidential Election in 2019

Hulliyah, Khodijah and Awang Abu Bakar, Normi Sham and Ismail, Amelia Ritahani and M. Octaviano, Pratama (2020) A benchmark of modeling for sentiment analysis of the Indonesian Presidential Election in 2019. In: 7th International Conference on Cyber and IT Service Management, CITSM 2019, 6-8 Nov. 2019, Jakarta Convention Center, Indonesia.

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Abstract

Researching with a machine learning method approach, the truth is to try to solve a case by using various algorithmic approaches to obtain the most suitable model for a case. In this research, we want to know which process of modelling that has the best accuracy value for classifying emotions in the text. The algorithm used is using the LSTM algorithm, while the benchmarking that we tested is the Random Forest and Naive Bayes algorithm. This research takes public opinion about the 2019 Indonesian Presidential Election by classifying it into four types of emotions: happy, sad, angry, and afraid. The data we use contains more than 1200 Indonesian tweets. In this experiment, we achieved an accuracy of 68.25% using the Random Forest model, whereas, with the Multinomial Naïve Bayes model, the accuracy was 66%.

Item Type:	Conference or Workshop Item (Plenary Papers)
Additional Information:	3509/86598
Uncontrolled Keywords:	Machine Learning, Emotion Word, Twitter, Random Forest, and Multinomial Naive Bayes
Subjects:	Q Science > QA Mathematics T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Normi Sham Awang Abu Bakar
Date Deposited:	18 Dec 2020 10:55
Last Modified:	23 Mar 2021 13:02
URI:	http://irep.iium.edu.my/id/eprint/86598

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