How to use AI tools to help analyze keywords in academic articles?

AI tools enhance keyword analysis in academic articles through automated text mining and natural language processing (NLP) capabilities, offering significant efficiency gains over manual methods. This approach is both technically feasible and increasingly accessible. Effective implementation relies on robust NLP algorithms that identify frequent terms, phrases, and semantically related concepts. Key requirements include converting PDFs to machine-readable text and selecting appropriate tools, such as dedicated academic search platforms with built-in analytics or scriptable libraries like spaCy or scikit-learn. Considerations involve ensuring contextual relevance, mitigating extraction errors, using supplementary human validation for accuracy, and recognizing that AI identifies surface-level patterns rather than deep conceptual novelty within the text. Typical implementation follows a four-step workflow: First, ingest article texts via PDF parsers. Second, preprocess text through tokenization, lemmatization, and stop word removal. Third, employ algorithms like TF-IDF, RAKE, or LDA models to extract and rank candidate keywords or topic clusters. Fourth, visualize results through word clouds, cluster maps, or network graphs. This streamlines literature reviews, reveals research trends, and aids in article indexing and discovery.