Comparative Analysis of Proposed CNN Performance with CNN and Naive Bayes from Kaggle in ChatGPT Tweet Sentiment Analysis - Alwi Pratama; Ario Yudo Husodo; Fitri Bimantoro (Informatics Engineering Dept., Faculty of Engineering, University of Mataram, 2025)
The rapid growth of social media platforms such as Twitter has led to an increasing demand for efficient sentiment analysis methods. This study focuses on the performance comparison of the CNN-based sentiment analysis model developed by the authors with two models sourced from Kaggle; CNN model and Naive Bayes model. In addition, ChatGPT is used as a reference in discourse exploration and sentiment analysis strategy development. ChatGPT is used to answer user questions, generate code, revise journals and the like. Performance evaluation is done in terms of inference time and accuracy. The findings reveal that the CNN model developed by the authors achieves superior accuracy compared to the CNN model from Kaggle, while the inference time developed by the authors shows a significant difference with a much higher number when compared to the Naive Bayes model from Kaggle. This analysis highlights the trade-off between efficiency and accuracy in sentiment analysis tasks and provides insights for selecting the right model based on current trends in data analysis.