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Sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning

Health Safety & Environment (HSE) situational awareness is a very important aspect of any risky workplace. Negligence in complying with HSE policies and practices might lead to unwanted incidents, critical injuries, death, spread of diseases and environmental pollution. In most corporations, inf...

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Main Authors: Dafaallah, D.E., Hashim, A.S.
Format: Article
Published: Engg Journals Publications 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094810514&doi=10.21817%2findjcse%2f2020%2fv11i5%2f201105244&partnerID=40&md5=dbe17cbecf5075c11527d6f8838e0344
http://eprints.utp.edu.my/23077/
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spelling my.utp.eprints.230772021-08-19T05:27:36Z Sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning Dafaallah, D.E. Hashim, A.S. Health Safety & Environment (HSE) situational awareness is a very important aspect of any risky workplace. Negligence in complying with HSE policies and practices might lead to unwanted incidents, critical injuries, death, spread of diseases and environmental pollution. In most corporations, information on HSE related incidents is disseminated through formal channels such as reports. Employees on the other hand frequently use social media to share, complain and discuss HSE-related issues. The issues are discussed through an informal platform, it is difficult to analyze opinions for further action. Therefore, this study will investigate existing sentiment analysis models and formulate a suitable sentiment analysis model using machine learning technique. Through literature review, Naïve Bayes model was found to be the most efficient text classification in sentiment analysis. This technique still needs further enhancement as the accuracy is not within requirement. Upon enhancing the Naïve Bayes model, a better outcome can be attained. © 2020, Engg Journals Publications. All rights reserved. Engg Journals Publications 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094810514&doi=10.21817%2findjcse%2f2020%2fv11i5%2f201105244&partnerID=40&md5=dbe17cbecf5075c11527d6f8838e0344 Dafaallah, D.E. and Hashim, A.S. (2020) Sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning. Indian Journal of Computer Science and Engineering, 11 (5). pp. 640-645. http://eprints.utp.edu.my/23077/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Health Safety & Environment (HSE) situational awareness is a very important aspect of any risky workplace. Negligence in complying with HSE policies and practices might lead to unwanted incidents, critical injuries, death, spread of diseases and environmental pollution. In most corporations, information on HSE related incidents is disseminated through formal channels such as reports. Employees on the other hand frequently use social media to share, complain and discuss HSE-related issues. The issues are discussed through an informal platform, it is difficult to analyze opinions for further action. Therefore, this study will investigate existing sentiment analysis models and formulate a suitable sentiment analysis model using machine learning technique. Through literature review, Naïve Bayes model was found to be the most efficient text classification in sentiment analysis. This technique still needs further enhancement as the accuracy is not within requirement. Upon enhancing the Naïve Bayes model, a better outcome can be attained. © 2020, Engg Journals Publications. All rights reserved.
format Article
author Dafaallah, D.E.
Hashim, A.S.
spellingShingle Dafaallah, D.E.
Hashim, A.S.
Sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning
author_facet Dafaallah, D.E.
Hashim, A.S.
author_sort Dafaallah, D.E.
title Sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning
title_short Sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning
title_full Sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning
title_fullStr Sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning
title_full_unstemmed Sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning
title_sort sentiment analysis techniques to analyze hse situational awareness at oil and gas platforms using machine learning
publisher Engg Journals Publications
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094810514&doi=10.21817%2findjcse%2f2020%2fv11i5%2f201105244&partnerID=40&md5=dbe17cbecf5075c11527d6f8838e0344
http://eprints.utp.edu.my/23077/
_version_ 1738656420937596928
score 13.211869

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