Aspect-based sentiment analysis towards technical and vocational education and training in Malaysia

Initiatives to improve public opinion towards technical and vocational education and training (TVET) have been increased by the government of Malaysia. However, to observe these sentiments with more transparent, analysis on public opinion is necessary. This research aims to assess the public sentime...

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Main Author:	Abd. Samad, Nurul Ashikin
Format:	Thesis
Language:	English
Published:	2019
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/96389/1/NurulAshikinAbdSamadMCS2019.pdf.pdf
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id	my-utm-ep.96389
record_format	uketd_dc
spelling	my-utm-ep.963892022-07-18T10:48:48Z Aspect-based sentiment analysis towards technical and vocational education and training in Malaysia 2019 Abd. Samad, Nurul Ashikin QA75 Electronic computers. Computer science Initiatives to improve public opinion towards technical and vocational education and training (TVET) have been increased by the government of Malaysia. However, to observe these sentiments with more transparent, analysis on public opinion is necessary. This research aims to assess the public sentiment regarding to TVET in Malaysia by performing aspect-based sentiment analysis. This study took advantage of the data availability from social media where public nowadays tend to express their feelings towards any products and services. Twitter appears as one of the most common social media platforms in which, countless of users can participate and interact at any time. The data from Twitter are unstructured by nature thus further mechanism are needed to provide more meaningful information for future uses. A series of text pre-processing strategies were implemented in this study to improve the process of aspect extraction and classification. Topic modelling technique, Latent Dirichlet Allocation (LDA) was used to extract aspect category during aspect extraction process. The lexicon-based classifiers; SentiWordNet (SWN) and Valence Aware Dictionary and Sentiment Reasoner (VADER) and machine learning classifiers; Naïve Bayes (NB) and Support Vector Machine (SVM) were used to classify the tweets sentiments. The performance of the classifiers was observed based on the results of precision, recall, f-measure, and accuracy. The finding revealed that the public sentiment for five (5) identified aspects for TVET in Malaysia; Student, Course, Employability, Skill and Accreditation inclined towards positive sentiments. SVM shows the highest accuracy among other classifiers with an acceptable accuracy of 72%. The results from this study were expected to give beneficial insight for TVET stakeholders specially the governing bodies and TVET providers to plan for improvisation strategies. 2019 Thesis http://eprints.utm.my/id/eprint/96389/ http://eprints.utm.my/id/eprint/96389/1/NurulAshikinAbdSamadMCS2019.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:143443 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Abd. Samad, Nurul Ashikin Aspect-based sentiment analysis towards technical and vocational education and training in Malaysia
description	Initiatives to improve public opinion towards technical and vocational education and training (TVET) have been increased by the government of Malaysia. However, to observe these sentiments with more transparent, analysis on public opinion is necessary. This research aims to assess the public sentiment regarding to TVET in Malaysia by performing aspect-based sentiment analysis. This study took advantage of the data availability from social media where public nowadays tend to express their feelings towards any products and services. Twitter appears as one of the most common social media platforms in which, countless of users can participate and interact at any time. The data from Twitter are unstructured by nature thus further mechanism are needed to provide more meaningful information for future uses. A series of text pre-processing strategies were implemented in this study to improve the process of aspect extraction and classification. Topic modelling technique, Latent Dirichlet Allocation (LDA) was used to extract aspect category during aspect extraction process. The lexicon-based classifiers; SentiWordNet (SWN) and Valence Aware Dictionary and Sentiment Reasoner (VADER) and machine learning classifiers; Naïve Bayes (NB) and Support Vector Machine (SVM) were used to classify the tweets sentiments. The performance of the classifiers was observed based on the results of precision, recall, f-measure, and accuracy. The finding revealed that the public sentiment for five (5) identified aspects for TVET in Malaysia; Student, Course, Employability, Skill and Accreditation inclined towards positive sentiments. SVM shows the highest accuracy among other classifiers with an acceptable accuracy of 72%. The results from this study were expected to give beneficial insight for TVET stakeholders specially the governing bodies and TVET providers to plan for improvisation strategies.
format	Thesis
qualification_level	Master's degree
author	Abd. Samad, Nurul Ashikin
author_facet	Abd. Samad, Nurul Ashikin
author_sort	Abd. Samad, Nurul Ashikin
title	Aspect-based sentiment analysis towards technical and vocational education and training in Malaysia
title_short	Aspect-based sentiment analysis towards technical and vocational education and training in Malaysia
title_full	Aspect-based sentiment analysis towards technical and vocational education and training in Malaysia
title_fullStr	Aspect-based sentiment analysis towards technical and vocational education and training in Malaysia
title_full_unstemmed	Aspect-based sentiment analysis towards technical and vocational education and training in Malaysia
title_sort	aspect-based sentiment analysis towards technical and vocational education and training in malaysia
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2019
url	http://eprints.utm.my/id/eprint/96389/1/NurulAshikinAbdSamadMCS2019.pdf.pdf
_version_	1747818663168180224

Aspect-based sentiment analysis towards technical and vocational education and training in Malaysia

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