Slangs And Short Forms Of Malay Twitter Sentiment Analysis Using Supervised Machine Learning

Cheng, Jet Yin and Ayop, Zakiah and Anawar, Syarulnaziah and Othman, Nur Fadzilah and Mohd Zainudin, Norulzahrah (2021) Slangs And Short Forms Of Malay Twitter Sentiment Analysis Using Supervised Machine Learning. International Journal of Computer Science and Network Security, 21 (11). pp. 294-300. ISSN 1738-7906

Text 2.3.1.1.2 IJCSNS-SLANGS AND SHORT FORMS OF MALAY TWITTER SENTIMENT ANALYSIS USING SUPERVISED MACHINE LEARNING.PDF Download (256kB)

Abstract

The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This paper is to determine which of the algorithms chosen in supervised machine learning with higher accuracy in detecting internet slang and short forms. To analyze the results of the supervised machine learning classifiers, we have chosen two types of datasets, one is political topic-based, and another same set but is mixed with 50 tweets per targeted keyword. The datasets are then manually labelled positive and negative, before separating the 275 tweets into training and testing sets. Naïve Bayes and Random Forest classifiers are then analyzed and evaluated from their performances. Our experiment results show that Random Forest is a better classifier compared to Naïve Bayes.

Item Type:	Article
Uncontrolled Keywords:	Naïve bayes, Random forest, Supervised machine learning, Twitter
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	28 Feb 2022 13:12
Last Modified:	28 Feb 2022 13:12
URI:	http://eprints.utem.edu.my/id/eprint/25548
Statistic Details:	View Download Statistic

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