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...
Saved in:
Main Authors: | , , |
---|---|
Other Authors: | |
Format: | Conference Paper |
Published: |
Institute of Electrical and Electronics Engineers Inc.
2024
|
Subjects: | |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uniten.dspace-34395 |
---|---|
record_format |
dspace |
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 |