Sentiment Analysis Techniques to Analyze Hse Situational Awareness at Oil and Gas Platforms Using Machine Learning

Abstract

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.

A Sentiment Analysis Method for Teaching Evaluation Texts Using Attention Mechanism Combined with CNN-BLSTM Model

“…Analyzing and studying student emotions through evaluation texts has important practical significance and research value in understanding students' mental state and information retrieval [5]. However, because human emotions are very complex and student emotions are even more diverse, research is relatively difficult, and it is still in its infancy [6]. Existing studies often use hand-designed feature extraction methods to extract text features that affect students' emotions, ignoring the word order and context information of the text and failing to capture the complex language phenomena in the text.…”