This paper systematically evaluates multiple NLP algorithms—BOW, TF-IDF, GloVe, and SBERT—combined with machine learning classifiers for automating HAZOP report analysis. GloVe with Random Forest achieved the highest accuracy at 83%, while a zero-shot classification model achieved only 52%, demonstrating that domain-specific fine-tuning remains essential. For MOC, these findings are critical because every process change that could affect safety requires hazard identification and assessment. This research establishes which NLP techniques are most effective for automatically processing HAZOP data under data-scarce conditions typical of MOC reviews, enabling faster turnaround of change requests while maintaining analytical rigor in hazard and operability evaluations.
