Examining the Runtime of NLTK and TensorFlow Algorithms for Chatbot Based on intents

This research paper investigates the relationship between the length of the intents.json file and the runtime of NLTK (Natural Language Toolkit) and TensorFlow algorithms for chatbot development in Python. By examining the runtime efficiency based on different intents.json file sizes, the study aims to determine the impact of file length on algorithm performance. Experimental analysis reveals a linear relationship between intents.json length and training runtime, indicating an O(N) runtime complexity. This research provides valuable implications for developers seeking to enhance the runtime performance of chatbot systems by providing them with a baseline of runtime for chatbot dynamic creation - giving organizations the opportunity to properly allocate resources towards chatbot development.