How to use AI tools to model the topics of academic articles?

AI tools enable automated topic modeling of academic articles through natural language processing techniques. These methods efficiently identify latent themes and patterns within large collections of texts. Effective topic modeling requires preprocessing text data, including tokenization and lemmatization. Key algorithms such as Latent Dirichlet Allocation (LDA) or transformer-based models like BERTopic analyze word distributions to infer topics. Input quality significantly impacts output accuracy, necessitating curated, relevant corpora. Domain specificity must be considered to ensure meaningful results. Crucially, generated topics require validation and interpretation by researchers to guarantee scholarly relevance and coherence. To implement, researchers first preprocess their article dataset. They then select and configure a suitable AI algorithm, specifying parameters like the target number of topics. Running the model outputs topic-term distributions and article-topic probabilities. Results are typically analyzed through visualizations and key terms. This approach accelerates literature reviews, aids research gap identification, and supports large-scale content analysis by significantly reducing manual screening time while uncovering thematic structures.