• Ishant Kundra
• Jeremy Jason Kujur
• Chirag Soni

Abstract---Natural Language Processing (NLP)-based chatbots have become important resources for easing human-computer communication. This research paper describes the creation and assessment of a chatbot that engages users in lively discussions using NLP approaches. The main goal of this research was to develop an effective and precise chatbot that could comprehend user inquiries and give appropriate, contextually relevant answers. To Design a ML / CL based Chatbot utility which can help professionals to highlightthesafety risk as per the incident description. For the Data analytics, extensive Database createdfrom one of the biggest Metals & Mining Industry in Brazil which has various plants across World. The database is basically records of accidents from 12 different plants in 03 different countries which every line in the data is an occurrence of an accident. In the high-stakes domain of industrial safety and health analysis, particularly within the intricate workings of a chemical plant, the imperative for precision, expertise, and immediate response is paramount. It is in this context that we introduce our specialized chatbot, tailored to address the unique challenges and complexities inherent to Metal and Mining plant operations. This innovative chatbot serves as a pivotal tool in ensuring the well-being of both the workforce and the surrounding environment. Its mission is twofold: to proactively identify and mitigate potential hazards, and to offer swift guidance and support in the event of an unforeseen incident. As the chemical industry continues to evolve and expand, necessitating stringent adherence to safety protocols and environmental regulations, our chatbot emerges as a dedicated and knowledgeable partner, capable of navigating the intricate landscape of chemical plant safety and health with unparalleled efficacy. In this paper, we elucidate the development, capabilities, and distinct advantages of our chatbot within this specialized context, highlighting its pivotal role in safeguarding lives, assets, and the broader community. A dataset including accident descriptions and meta-data was used in the study. To handle textual data efficiently, NLP techniques including text preparation, tokenization, and lemmatization were used. A text-input model that exclusively used accident descriptions was created, and it was compared against a multiple-input model that included both textual and category data. In pursuit of these objectives, this research leverages data-driven methodologies and cutting-edge technology to examine patterns, identify risk factors, and propose strategies for proactive intervention. Our aim is to contribute to the growing body of knowledge surrounding industrial safety and health, catalyzing advancements that can safeguard lives and livelihoods. Utilising classification criteria, such as accuracy and F1-score, the models were trained and assessed. The outcomes showed that the multiple-input model beat the text-input model, obtaining a test accuracy and an F1-score on the original dataset of 73.81% and 73.81%, respectively. Additionally, learning curves and confusion matrices were used to illustrate the chatbot's performance, proving its capacity for generalisation and preventing overfitting. The construction of the chatbot successfully demonstrates the value of NLP methods in boosting human-computer interactions. This study adds to the increasing body of knowledge in natural language processing and illustrates the promise of chatbots in a number of fields, such as customer service, information retrieval, and interactive interfaces. Future development might concentrate on enhancing the chatbot's capabilities, including new NLP tools, and improving its answer generation for more complex conversations.

Keywords: Chatbot, Natural Language Processing, NLP, Text Classification, Deep Learning, Machine Learning, Human-Computer Interaction.

"Chatbot for Industrial Safety and Health Analytics Database using NLP and Machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.g784-g805, December-2023, Available :http://www.jetir.org/papers/JETIR2312694.pdf

"Chatbot for Industrial Safety and Health Analytics Database using NLP and Machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppg784-g805, December-2023, Available at : http://www.jetir.org/papers/JETIR2312694.pdf