eprintid: 8659 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/86/59 datestamp: 2023-09-05 23:30:18 lastmod: 2023-09-05 23:30:19 status_changed: 2023-09-05 23:30:18 type: article metadata_visibility: show creators_name: R, Sudheesh creators_name: Mujahid, Muhammad creators_name: Rustam, Furqan creators_name: Shafique, Rahman creators_name: Chunduri, Venkata creators_name: Gracia Villar, Mónica creators_name: Brito Ballester, Julién creators_name: Diez, Isabel de la Torre creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: monica.gracia@uneatlantico.es creators_id: julien.brito@uneatlantico.es creators_id: creators_id: title: Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach ispublished: pub subjects: uneat_eng subjects: uneat_mm divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: ChatGPT; sentimental analysis; BERT; machine learning; LDA; app reviewers; deep learning 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%. date: 2023-08 publication: Information volume: 14 number: 9 pagerange: 474 id_number: doi:10.3390/info14090474 refereed: TRUE issn: 2078-2489 official_url: http://doi.org/10.3390/info14090474 access: open language: en citation: Artículo Materias > Ingeniería Materias > Comunicación Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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%. metadata R, Sudheesh; Mujahid, Muhammad; Rustam, Furqan; Shafique, Rahman; Chunduri, Venkata; Gracia Villar, Mónica; Brito Ballester, Julién; Diez, Isabel de la Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, julien.brito@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach. Information, 14 (9). p. 474. ISSN 2078-2489 document_url: http://repositorio.unini.edu.mx/id/eprint/8659/1/information-14-00474-v2.pdf