• Akash Gundapuneni
• Aishwarya Yelamareddy
• C Vishnu
• Anjali Choudary Mutyala

The research paper titled "Optimizing Chatbot Performance: A Comparative Study of Training Algorithms" delves into the effectiveness of four prominent algorithms—Natural Language Processing (NLP), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), and Support Vector Machine (SVM)—in enhancing chatbot performance within customer service applications. Given the increasing integration of chatbots as vital tools in customer support, the choice of underlying algorithms significantly influences their capacity to accurately and efficiently understand and respond to user queries. The study conducts a thorough comparative analysis, with a primary focus on NLP's language comprehension, sentiment analysis, and intent recognition capabilities. Additionally, it scrutinizes LSTM for its memory functions, RNN for sequential data processing, and SVM for text classification. Employing criteria such as accuracy, response time, scalability, and resource utilization, the research evaluates each algorithm's performance in real-world customer service scenarios, providing practical insights for organizations seeking to optimize their chatbot functionality. By delineating the strengths and weaknesses of these algorithms, the study offers guidance for selecting the most suitable approach tailored to specific use cases. Ultimately, the research contributes valuable insights to the field of chatbot development and AI-driven customer service, facilitating informed decision-making in algorithm selection and elevating the overall quality of customer interactions.

Chatbot, customer service, Algorithm testing, Natural Language Processing (NLP), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), and Support Vector Machine (SVM)

"Optimizing Chatbot Performance: A Comparative Study of Training Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.g612-g619, January-2024, Available :http://www.jetir.org/papers/JETIR2401674.pdf

"Optimizing Chatbot Performance: A Comparative Study of Training Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppg612-g619, January-2024, Available at : http://www.jetir.org/papers/JETIR2401674.pdf