| Understanding the sentiment trends of large and unstructured text corpora is essential for various applications. Despite extensive application of sentiment analysis and topic modeling, extracting meaningful insights from the vast amount of textual data generated on social media platforms presents unique challenges due to the short and noisy nature of the text. In this study, we propose a methodology for analyzing sentiment trends in social media, including data collection, data preprocessing, sentiment analysis, social network graph construction, and topic modeling interpretation using ChatGPT. By integrating ChatGPT with topic modeling techniques such as LDA and BERTopic, we aim to enhance the interpretability of sentiment-related topics and gain deeper insights into sentiment trends in social media conversations. Through a case study focusing on parental hesitancy toward child vaccination, we illustrate the applicability and utility of our proposed methodology in real-world social media analysis scenarios, demonstrating its effectiveness in topic modeling interpretation and enhancing understanding of social media discourse. The integration of ChatGPT and BERTopic yielded improved topic interpretation for the short text of large corpus based on the coherence score of the original posts and generated description of the topic, ultimately reducing the cost and time required for topic interpretation by humans. |
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