Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language

Abstract

ChatGPT (Generative Pre-Trained Transformer) is a chatbot that is being widely used by the public. This technology is based on Artificial Intelligence and is capable of having conversational interactions with its users just like humans, but in the form of automated text. Because of this capability, online forums such as Brainly and the like can be overtaken by these smart chatbots. Therefore, this study was conducted to determine the positive and negative sentiments towards ChatGPT using Naive Bayes Classification algorithm on 5000 Twitter users. Data was collected by scraping technique and Python programming language was used in data analysis. The results showed that the majority of Twitter users had a positive sentiment of 57.6% towards ChatGPT, while the negative sentiment reached 42.4%. The resulting classification model had an accuracy of 80%, indicating a good classification model in determining sentiment probabilities. These findings provide a basis for the development of better AI chatbot technology that can meet user needs. The results of this study provide insights into user sentiment towards ChatGPT and can be used as a reference for future AI chatbot development.