Sentiment Analysis Classification of ChatGPT Tweets Using Machine Learning and Deep Learning Algorithms

Abstract

Sentiment analysis is essential for understanding public opinion and emotional responses to specific topics. In this study, we conduct sentiment analysis on a dataset comprising tweets related to ChatGPT. The dataset includes two primary columns: tweets and sentiment labels (positive, negative, and neutral_l). We developed and evaluated machine learning (ML) models to classify these tweets' sentiments. To preprocess the data, we applied standard text cleaning techniques such as removing special characters, tokenization, and stop word removal. The textual data was converted via Count Vectorizer to numerical features, and the labels were encoded using Label Encoder to transform categorical sentiment labels into numerical values. The Convolutional Neural Network (CNN) captured sequential patterns within the tweets and achieved a noteworthy accuracy of 88.25%. The Long Short-Term Memory (LSTM) network has captured temporal dependencies and yielded an accuracy of 89.24%. Logistic Regression (LR) achieved an accuracy of 85.74%, while Decision Tree (DT) and Multinomial Naive Bayes (MNB) models achieved 71.60% and 67% accuracy, respectively. The results demonstrate the efficacy of machine learning models, particularly CNN and LSTM, in accurately classifying the sentiment of ChatGPT-related tweets and effectively capturing sequential and temporal characteristics of social media text, offering insights into public sentiment towards ChatGPT. Our findings have practical implications for understanding user feedback on ChatGPT to enhance its performance and user experience on social platforms.