Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning

The study comprehensively analysed public sentiment towards ChatGPT, a language processing AI tool, using approximately 135,000 tweets from an initial dataset of 236,275 entries. After cleaning and pre-processing the data using basic NLP techniques, Machine Learning (ML) models—Long Short-Term Memory (LSTM), Decision Tree (DT), and Support Vector Machine (SVM)—were applied to classify the tweets into positive, negative, and neutral categories. The LSTM model demonstrated superior performance across all evaluation metrics—accuracy, precision, recall, and F1 score—for all tweet categories. Word clouds generated for each category revealed that the most frequent words in positive tweets included ‘student,’ 'education,’ 'research,’ and ‘teacher,’ indicating a strong association of ChatGPT with educational and research contexts. Similarly, ‘AI,’ ‘student,’ 'research,’ ‘course,’ and ‘education’ dominated neutral and negative tweets, highlighting prevalent discussion topics. However, the study had limitations, including dependency on a single dataset, potential loss of contextual information during pre-processing, and a focus on English tweets only. Despite these limitations, the study provides valuable insights into the public’s perception of ChatGPT, underscoring its significant association with education and research and the effectiveness of the LSTM model in classifying tweets. The findings suggest that future research should incorporate diverse data sources and languages better to understand global sentiment towards ChatGPT and similar AI tools.