Staff View: Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]

Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]

In an academic institution, deciphering the opinions of students is the key that ensures the institution continues to strive within the education industry. Extracting implicit information from student opinions are vital in ensuring the standard of education continuously improves, ultimately leading...

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Main Authors:	Kaur, Wandeep, Balakrishnan, Vimala
Format:	Article
Language:	English
Published:	Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2017
Subjects:	TJ Mechanical engineering and machinery Mechanics applied to machinery. Dynamics
Online Access:	http://ir.uitm.edu.my/id/eprint/39271/1/39271.pdf http://ir.uitm.edu.my/id/eprint/39271/
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id	my.uitm.ir.39271
record_format	eprints
spelling	my.uitm.ir.392712020-12-17T07:34:30Z http://ir.uitm.edu.my/id/eprint/39271/ Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.] Kaur, Wandeep Balakrishnan, Vimala TJ Mechanical engineering and machinery Mechanics applied to machinery. Dynamics In an academic institution, deciphering the opinions of students is the key that ensures the institution continues to strive within the education industry. Extracting implicit information from student opinions are vital in ensuring the standard of education continuously improves, ultimately leading to student retention and increase number of student intake within the institution. Sentiment analysis is a field of study that is interested in extracting sentiments from opinions extracted from written text. These techniques determine if an opinion is penchant towards positivity or negativity. The main aim of this paper is to conduct a preliminary analysis on the opinions of students taking Thermal Engineering (MEC551) from Universiti Teknologi Mara (UiTM) with regard to course tools. Data collected from Facebook was subjected to cleaning and pre-processing. A supervised machine learning algorithm was employed for sentiment classification purpose which was implemented using Rapid Miner. Algorithms were compared and results indicate Support Vector Machine (93.6%) outperformed Naïve Bayes (90.1%) and K-Nearest Neighbour (90.2%) in terms of accuracy and was able to correctly classify the text accordingly. This in return indicates students were very much interested in being able to interact and discuss on questions and queries via Facebook as well as address some fears they had related to exams and assignments seamlessly with their classmates as well as lecturer. Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2017 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/39271/1/39271.pdf Kaur, Wandeep and Balakrishnan, Vimala (2017) Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]. Journal of Mechanical Engineering (JMechE), SI 4 (1). pp. 263-272. ISSN 18235514
institution	Universiti Teknologi Mara
building	Tun Abdul Razak Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Mara
content_source	UiTM Institutional Repository
url_provider	http://ir.uitm.edu.my/
language	English
topic	TJ Mechanical engineering and machinery Mechanics applied to machinery. Dynamics
spellingShingle	TJ Mechanical engineering and machinery Mechanics applied to machinery. Dynamics Kaur, Wandeep Balakrishnan, Vimala Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]
description	In an academic institution, deciphering the opinions of students is the key that ensures the institution continues to strive within the education industry. Extracting implicit information from student opinions are vital in ensuring the standard of education continuously improves, ultimately leading to student retention and increase number of student intake within the institution. Sentiment analysis is a field of study that is interested in extracting sentiments from opinions extracted from written text. These techniques determine if an opinion is penchant towards positivity or negativity. The main aim of this paper is to conduct a preliminary analysis on the opinions of students taking Thermal Engineering (MEC551) from Universiti Teknologi Mara (UiTM) with regard to course tools. Data collected from Facebook was subjected to cleaning and pre-processing. A supervised machine learning algorithm was employed for sentiment classification purpose which was implemented using Rapid Miner. Algorithms were compared and results indicate Support Vector Machine (93.6%) outperformed Naïve Bayes (90.1%) and K-Nearest Neighbour (90.2%) in terms of accuracy and was able to correctly classify the text accordingly. This in return indicates students were very much interested in being able to interact and discuss on questions and queries via Facebook as well as address some fears they had related to exams and assignments seamlessly with their classmates as well as lecturer.
format	Article
author	Kaur, Wandeep Balakrishnan, Vimala
author_facet	Kaur, Wandeep Balakrishnan, Vimala
author_sort	Kaur, Wandeep
title	Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]
title_short	Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]
title_full	Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]
title_fullStr	Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]
title_full_unstemmed	Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]
title_sort	social media sentiment analysis of thermal engineering students for continuous quality improvement in engineering education / wandeep kaur ...[et al.]
publisher	Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM)
publishDate	2017
url	http://ir.uitm.edu.my/id/eprint/39271/1/39271.pdf http://ir.uitm.edu.my/id/eprint/39271/
_version_	1687396761540231168
score	13.211869

Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]

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