首页    期刊浏览 2025年02月17日 星期一
登录注册

文章基本信息

  • 标题:SENTIMENT ANALYSIS TECHNIQUES TO ANALYZE HSE SITUATIONAL AWARENESS AT OIL AND GAS PLATFORMS USING MACHINE LEARNING
  • 本地全文:下载
  • 作者:Dafuallah Esameldien Dafaallah ; Ahmad Sobri Hashim
  • 期刊名称:Indian Journal of Computer Science and Engineering
  • 印刷版ISSN:2231-3850
  • 电子版ISSN:0976-5166
  • 出版年度:2020
  • 卷号:11
  • 期号:5
  • 页码:640-645
  • DOI:10.21817/indjcse/2020/v11i5/201105244
  • 出版社:Engg Journals Publications
  • 摘要: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.
  • 关键词:HSE;Sentiment Analysis;Naïve Bayes;Machine Learning
国家哲学社会科学文献中心版权所有