Lacking data has always been a challenging problem for risk analysts on human and organizational factors (HOFs) since the theme comes to birth. Accident reports are an essential source of HOFs information, but they are often in the form of unstructured text, making it challenging to apply the number statistic method directly. The traditional manual coding of accident records could introduce uncertainties and inefficiencies, especially when a large number of records is available. Thanks to the development of the natural language processing (NLP) technique, some analysts have attempted to mine the text of accident reports (Single et al., 2020). A similar approach was adopted to highlight HOFs contributing to the accidents. The NLP and HOFs categories have then been introduced to obtain the critical structure of HOFs related accidents. Furthermore, the approach of text similarities calculation is applied to support the relationship analysis of performance influencing factors (PIF) based on the mining of data of the EU Major Accident Reporting System’s (eMARS). In general terms, a framework is proposed to efficiently exploit the information contained in accident records to assess the HOFs elements better to be included in process risk assessment.