ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification

Authors

  • Aliea Sabir Faculty of Computer Science and Information Technology, Computer Information systems Dept., University of Basrah, Basrah, Iraq
  • Huda Adil Ali Faculty of Computer Science and Information Technology, Computer Science Dept., University of Basrah, Basrah, Iraq
  • Maalim A. Aljabery Faculty of Computer Science and Information Technology, Computer Science Dept., University of Basrah, Basrah, Iraq

DOI:

https://doi.org/10.31449/inf.v48i7.5535

Abstract

Many things, such as goods, products, and websites are evaluated based on user's notes and comments. One popular research project is sentiment analysis, which aims to extract information from notes and comments as a natural language processing (NLP) to understand and express emotions. In this study we analyzed the sentiment of ChatGPT labeled tweet datasets sourced from the Kaggle community using five Machine Learning (ML) algorithms; decision tree, KNN, Naïve Bayes, Logistic Regression, and SVM. We applied three feature extraction techniques: positive/negative frequency, a bag of words (count vector), and TF IDF. For each classification algorithm. The results were assessed using accuracy measures. Our experiments achieved an accuracy of 96.41% with SVM classifier when using TF- IDF as a feature extraction technique.

Author Biographies

Aliea Sabir, Faculty of Computer Science and Information Technology, Computer Information systems Dept., University of Basrah, Basrah, Iraq

Aliea Sabir :received the B.S. and M.Sc. ,and Ph.D degrees in Computer Science from College of Science, University of Basrah, Basrah, IRAQ.  She specialized in information extraction from texts and natural  language processing . She can be contacted at email: aliea.sabir@uobasrah.edu.iq.

Huda Adil Ali, Faculty of Computer Science and Information Technology, Computer Science Dept., University of Basrah, Basrah, Iraq

Huda adil ali: received a bachelor's degree in computer science from Basrah University, Basrah, Iraq, and a Master's degree in computer science from Basrah University too. Her research areas of interest include image processing, security, steganography, NLP, Visualization, System analysis, and design. She can be contacted at Email: huda.ali@uobasrah.edu.iq

Maalim A. Aljabery, Faculty of Computer Science and Information Technology, Computer Science Dept., University of Basrah, Basrah, Iraq

Maalim A. Aljabery     received the B.S. and M.Sc. degrees in Computer Science from College of Science, University of Basrah, Basrah, IRAQ. She received the Ph.D degree in Electrical and Computer Engineering, Altinbas University, Istanbul, TURKEY. She specialized in Data Mining and Knowledge Discovery, Hearing Aids devices, and Audiology (field of medicine related with patients who suffer from speech-hearing problems in general). She can be contacted at email: maalim.aljabery@uobasrah.edu.iq.

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Published

2024-05-03

How to Cite

Sabir, A., Ali, H. A., & Aljabery, M. A. (2024). ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification. Informatica, 48(7). https://doi.org/10.31449/inf.v48i7.5535

Issue

Vol. 48 No. 7 (2024): Online-only issue

Section

Online-only

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