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Polarity Classification Tool for Sentiment Analysis in Malay Language

The popularity of the social media channels has increased the interest among researchers in the sentiment analysis (SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of...

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Main Authors: Awang Abu Bakar, Normi Sham, RAHMAT, ROS AZIEHAN, UTHMAN, UMAR FARUQ
Format: Article
Language:English
English
Published: 2019
Subjects:
T10.5 Communication of technical information
Online Access:http://irep.iium.edu.my/75242/1/Polarity%20Classification%20Tool%20for%20Sentiment%20Analysis%20in%20Malay%20Language.pdf
http://irep.iium.edu.my/75242/7/75242_Polarity%20classification%20tool%20for%20sentiment%20analysis%20in%20Malay%20language_Scopus.pdf
http://irep.iium.edu.my/75242/
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spelling my.iium.irep.752422019-11-24T13:35:29Z http://irep.iium.edu.my/75242/ Polarity Classification Tool for Sentiment Analysis in Malay Language Awang Abu Bakar, Normi Sham RAHMAT, ROS AZIEHAN UTHMAN, UMAR FARUQ T10.5 Communication of technical information The popularity of the social media channels has increased the interest among researchers in the sentiment analysis (SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool (MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data. 2019-09-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/75242/1/Polarity%20Classification%20Tool%20for%20Sentiment%20Analysis%20in%20Malay%20Language.pdf application/pdf en http://irep.iium.edu.my/75242/7/75242_Polarity%20classification%20tool%20for%20sentiment%20analysis%20in%20Malay%20language_Scopus.pdf Awang Abu Bakar, Normi Sham and RAHMAT, ROS AZIEHAN and UTHMAN, UMAR FARUQ (2019) Polarity Classification Tool for Sentiment Analysis in Malay Language. IAES International Journal of Artificial Intelligence (IJ-AI), 8 (3). pp. 258-263. ISSN 2252-8938 10.11591/ijai.v8.i3
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T10.5 Communication of technical information
spellingShingle T10.5 Communication of technical information
Awang Abu Bakar, Normi Sham
RAHMAT, ROS AZIEHAN
UTHMAN, UMAR FARUQ
Polarity Classification Tool for Sentiment Analysis in Malay Language
description The popularity of the social media channels has increased the interest among researchers in the sentiment analysis (SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool (MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.
format Article
author Awang Abu Bakar, Normi Sham
RAHMAT, ROS AZIEHAN
UTHMAN, UMAR FARUQ
author_facet Awang Abu Bakar, Normi Sham
RAHMAT, ROS AZIEHAN
UTHMAN, UMAR FARUQ
author_sort Awang Abu Bakar, Normi Sham
title Polarity Classification Tool for Sentiment Analysis in Malay Language
title_short Polarity Classification Tool for Sentiment Analysis in Malay Language
title_full Polarity Classification Tool for Sentiment Analysis in Malay Language
title_fullStr Polarity Classification Tool for Sentiment Analysis in Malay Language
title_full_unstemmed Polarity Classification Tool for Sentiment Analysis in Malay Language
title_sort polarity classification tool for sentiment analysis in malay language
publishDate 2019
url http://irep.iium.edu.my/75242/1/Polarity%20Classification%20Tool%20for%20Sentiment%20Analysis%20in%20Malay%20Language.pdf
http://irep.iium.edu.my/75242/7/75242_Polarity%20classification%20tool%20for%20sentiment%20analysis%20in%20Malay%20language_Scopus.pdf
http://irep.iium.edu.my/75242/
_version_ 1651865974680322048
score 13.211869

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