Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts’ subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

中文翻译：

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从非结构化事故报告到职业风险管理的混合决策支持系统：共识聚合方法

石油行业的工作场所事故可能会对人员、财产和环境造成灾难性损害。该领域的早期研究表明，大多数事故报告信息都以非结构化文本格式提供。事故数据分析的传统技术非常耗时，并且严重依赖于专家的学科知识、经验和判断。需要开发一种基于机器学习的决策支持系统来分析大量由于缺乏适当的方法而经常被忽视的非结构化文本数据。为了解决文献中的这一差距，我们提出了一种混合方法，该方法使用改进的文本挖掘技术与无偏见的群体决策框架相结合，将客观权重（基于文本挖掘）和主观权重（基于专家意见）的风险因素，以确定其优先顺序。基于上下文词嵌入模型和术语频率，我们提取了五个重要的风险因素簇，其中包含超过 32 个风险子因素。联系了石油行业的一组不同的专家和员工，以获取他们对提取的风险因素的意见，并使用最佳-最差方法将他们的意见转换为权重。我们提出的框架的适用性通过印度石油行业发布的事故数据汇编的数据进行了测试。我们的框架可以扩展到任何行业的事故数据，以减少分析时间并提高风险因素分类和优先级的准确性。