Sentiment Analysis on Mixed-Language Social Media Post

Sentiment analysis is a powerful tool that can be used by businesses and organizations to gather valuable data about public opinion towards a brand, product, topic, event, and much more. However, most Malaysians post on social media using a mix of English and Malay words, also known as Manglish, whi...

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Main Authors: Mohamed Salleh F.H., Gorment N.Z., Mohd Ridza M.H.
Other Authors: 58883881800
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2024
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spelling my.uniten.dspace-343952024-10-14T11:19:29Z Sentiment Analysis on Mixed-Language Social Media Post Mohamed Salleh F.H. Gorment N.Z. Mohd Ridza M.H. 58883881800 57201987388 58883798500 machine learning mixed-language sentiment analysis social media post Learning algorithms Machine learning Social aspects Social networking (online) Analysis models Machine-learning Malaysians Mixed-language Power Public opinions Sentiment analysis Social media Social medium post Topic events Sentiment analysis Sentiment analysis is a powerful tool that can be used by businesses and organizations to gather valuable data about public opinion towards a brand, product, topic, event, and much more. However, most Malaysians post on social media using a mix of English and Malay words, also known as Manglish, which are not catered to by existing sentiment analysis models. Malaysian-centric companies that are interested to analyze the Malaysian posts would have to do so manually, which is costly and time-consuming. Motivated by this issue, this paper aims to propose a method of performing sentiment analysis on posts using the power of machine learning. Several machine learning algorithms were identified and trained to classify a Manglish post as either positive or negative. Steps are also taken to ensure the reliability of the model and to improve it after the first training experiment. We found that this method is successful in producing a model that can predict social media post sentiment with reliable and acceptable accuracy. � 2023 IEEE. Final 2024-10-14T03:19:29Z 2024-10-14T03:19:29Z 2023 Conference Paper 10.1109/ICOCO59262.2023.10397971 2-s2.0-85184854834 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184854834&doi=10.1109%2fICOCO59262.2023.10397971&partnerID=40&md5=8ec24a22f5321ccb307f4a5c44e8d6a5 https://irepository.uniten.edu.my/handle/123456789/34395 420 425 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic machine learning
mixed-language
sentiment analysis
social media post
Learning algorithms
Machine learning
Social aspects
Social networking (online)
Analysis models
Machine-learning
Malaysians
Mixed-language
Power
Public opinions
Sentiment analysis
Social media
Social medium post
Topic events
Sentiment analysis
spellingShingle machine learning
mixed-language
sentiment analysis
social media post
Learning algorithms
Machine learning
Social aspects
Social networking (online)
Analysis models
Machine-learning
Malaysians
Mixed-language
Power
Public opinions
Sentiment analysis
Social media
Social medium post
Topic events
Sentiment analysis
Mohamed Salleh F.H.
Gorment N.Z.
Mohd Ridza M.H.
Sentiment Analysis on Mixed-Language Social Media Post
description Sentiment analysis is a powerful tool that can be used by businesses and organizations to gather valuable data about public opinion towards a brand, product, topic, event, and much more. However, most Malaysians post on social media using a mix of English and Malay words, also known as Manglish, which are not catered to by existing sentiment analysis models. Malaysian-centric companies that are interested to analyze the Malaysian posts would have to do so manually, which is costly and time-consuming. Motivated by this issue, this paper aims to propose a method of performing sentiment analysis on posts using the power of machine learning. Several machine learning algorithms were identified and trained to classify a Manglish post as either positive or negative. Steps are also taken to ensure the reliability of the model and to improve it after the first training experiment. We found that this method is successful in producing a model that can predict social media post sentiment with reliable and acceptable accuracy. � 2023 IEEE.
author2 58883881800
author_facet 58883881800
Mohamed Salleh F.H.
Gorment N.Z.
Mohd Ridza M.H.
format Conference Paper
author Mohamed Salleh F.H.
Gorment N.Z.
Mohd Ridza M.H.
author_sort Mohamed Salleh F.H.
title Sentiment Analysis on Mixed-Language Social Media Post
title_short Sentiment Analysis on Mixed-Language Social Media Post
title_full Sentiment Analysis on Mixed-Language Social Media Post
title_fullStr Sentiment Analysis on Mixed-Language Social Media Post
title_full_unstemmed Sentiment Analysis on Mixed-Language Social Media Post
title_sort sentiment analysis on mixed-language social media post
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814060092600877056
score 13.209306