Sharma, A.Kumar, A.Vardhan, H.Mangalpady, A.Mandal, B.B.Senapati, A.Akhil, A.Saini, S.2026-02-032025Mining, Metallurgy and Exploration, 2025, 42, 6, pp. 4155-416725243462https://doi.org/10.1007/s42461-025-01351-9https://idr.nitk.ac.in/handle/123456789/19936Mining remains one of the most hazardous industries globally, marked by frequent fatalities resulting from complex operational risks. While accident investigation reports hold valuable insights for improving safety practices, the manual coding of fatality narratives remains labor-intensive, inconsistent, and impractical for large datasets. Although natural language processing (NLP) and machine learning (ML) techniques have gained traction for automating the analysis of safety narratives in other high-risk industries, their application to mining accident data, particularly within the Indian context, remains limited. Addressing this gap, the present study proposes a ML framework for the semi-automated classification of fatal accident causes from unstructured text narratives reported by the Directorate General of Mines Safety (DGMS) between 2016 and 2022. A total of 401 fatal accident descriptions were pre-processed and vectorized using Bag-of-Words, TF-IDF, and Word2Vec techniques, followed by model evaluation across multiple algorithms. A semi-automated classification scheme was developed to balance efficiency with expert oversight, where high-confidence predictions were assigned automatically and uncertain cases were flagged for manual review. Logistic regression combined with TF-IDF unigram features achieved the highest performance, with an F1 score of 0.78 and an accuracy of 0.81. Overall, the developed framework successfully auto-coded 68.75% of cases with 94% accuracy, 0.93 recall, and 0.91 precision. Word cloud visualizations were also employed to capture dominant words associated with different cause categories. The proposed framework offers a practical and operationally feasible solution for assigning fatality causes in the mining sector, contributing to active safety management, surveillance, and policy formulation. © Society for Mining, Metallurgy & Exploration Inc. 2025.Accident preventionAccidentsClassification (of information)Codes (symbols)Large datasetsLearning algorithmsLearning systemsLogistic regressionMachine learningMine safetyMiningNatural language processing systemsOccupational diseasesOccupational risksText processingAccident narrative classificationAutomated codingHILDALanguage processingMachine-learningMining accidentNatural language processingNatural languagesOccupational health and safetySemi-automated codingAutomationRisk assessment