Exploring the application of ChatGPT in scientific topic analysis: a novel paradigm for enhanced analysis and efficiency

Latent Dirichlet Allocation (LDA) is a powerful text analysis tool that has been widely used in literature to reveal the development trends of disciplines and fields, thereby greatly broadening the frontier of text mining and knowledge discovery. However, as a probability model based on word frequency statistics, LDA has inherent limitations in its inability to deeply understand the deep meaning of words in a document set. Although some researchers have attempted to combine LDA with other deep learning models, such as BERT and BiLSTM, in order to improve the effectiveness of topic modeling, the progress achieved has not been significant. In this study, we innovatively propose to combine the text comprehension ability of ChatGPT with the statistical ability of LDA model, aiming to further improve the accuracy and depth of topic modeling. Specifically, we first conduct topic modeling on the target text using the LDA topic model to obtain a topic-word matrix. Then, we input the word set corresponding to each topic in the matrix into the ChatGPT model with an appropriate prompt template to obtain a topic name-description table that accurately describes the topic. Finally, we input the content of each target text and the corresponding topic name-description table into the ChatGPT model to obtain the topic classification result for each text. In addition, we also conduct quantitative evaluation on the proposed method through calculating similarity based on BERT's word embedding vector. The experimental results show that our proposed ChatGPT + LDA method can significantly enhance the effectiveness of topic modeling, bringing new breakthroughs to the field of text analysis and knowledge discovery.