Analyzing User's Sentiments Using Twitter Data on the Usage of ChatGPT

Abstract

Because of the rapid advancement of Artificial Intelligence (AI) and natural language processing, powerful generative AI models have been built. ChatGPT, developed by OpenAI, is one of them. Its goal is to generate human-like text responses and participate in natural language conversations. It is critical to investigate public sentiment towards this cutting-edge technology because it is critical for improving ChatGPT interactions, addressing real-world applications in a variety of industries, advancing natural language interpretation, strengthening system robustness, and resolving ethical issues. As a result, the primary goal of this research is to examine people's perspectives about ChatGPT. a key AI language model using deep learning techniques and a machine learning technique with the help of Twitter data. The research collected and preprocessed 217,622 Twitter data before extracting features using the TF-IDF, Word2Vec, Doc2Vec, and GloVe. Then, it compares three algorithms: Long Short-Term Memory (LSTM), Artificial Neural Networks (ANN) Support Vector Machines (SVM). The results were validated by training and testing data and these algorithms' performance is measured using accuracy, precision, recall, F-score, and error values. The research seeks to determine the most successful algorithm with feature extraction combination through an evaluation. The results show that LSTM with TF-IDF feature extraction outperforms other techniques by 77.7%. The study gives light on public perceptions of ChatGPT, providing insights for responsible AI use and ethical considerations for the relevant parties. Based on that they can do necessary updates. Future research could look into employing ensemble learning methodologies for improve accuracy.