@article{uninimx8659,
            year = {2023},
         journal = {Information},
           month = {Agosto},
           pages = {474},
          number = {9},
          volume = {14},
          author = {Sudheesh R and Muhammad Mujahid and Furqan Rustam and Rahman Shafique and Venkata Chunduri and M{\'o}nica Gracia Villar and Juli{\'e}n Brito Ballester and Isabel de la Torre Diez and Imran Ashraf},
           title = {Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach},
             url = {http://repositorio.unini.edu.mx/id/eprint/8659/},
        abstract = {Chatbots are AI-powered programs designed to replicate human conversation. They are capable of performing a wide range of tasks, including answering questions, offering directions, controlling smart home thermostats, and playing music, among other functions. ChatGPT is a popular AI-based chatbot that generates meaningful responses to queries, aiding people in learning. While some individuals support ChatGPT, others view it as a disruptive tool in the field of education. Discussions about this tool can be found across different social media platforms. Analyzing the sentiment of such social media data, which comprises people?s opinions, is crucial for assessing public sentiment regarding the success and shortcomings of such tools. This study performs a sentiment analysis and topic modeling on ChatGPT-based tweets. ChatGPT-based tweets are the author?s extracted tweets from Twitter using ChatGPT hashtags, where users share their reviews and opinions about ChatGPT, providing a reference to the thoughts expressed by users in their tweets. The Latent Dirichlet Allocation (LDA) approach is employed to identify the most frequently discussed topics in relation to ChatGPT tweets. For the sentiment analysis, a deep transformer-based Bidirectional Encoder Representations from Transformers (BERT) model with three dense layers of neural networks is proposed. Additionally, machine and deep learning models with fine-tuned parameters are utilized for a comparative analysis. Experimental results demonstrate the superior performance of the proposed BERT model, achieving an accuracy of 96.49\%.},
        keywords = {ChatGPT; sentimental analysis; BERT; machine learning; LDA; app reviewers; deep learning}
}