Sentence-Level Insights from the Martian Literature: A Natural Language Processing Approach

Mars has been a primary focus of planetary science, with significant advancements over the past two decades across disciplines including geological evolution, surface environment, and atmospheric and space science. However, the rapid growth of the related literature has rendered traditional manual review methods increasingly inadequate. This inadequacy is particularly evident in interdisciplinary research, which is often characterized by dispersed topics and complex semantics. To address this challenge, this study proposes an automated analysis framework based on natural language processing (NLP) to systematically review the Martian research in Earth and space science over the past two decades. The research database contains 151,196 Mars-related sentences extracted from 10,655 publications spanning 2001 to 2024. Using machine learning techniques, the framework clusters Mars-related sentences into semantically coherent groups and applies topic modeling to extract core research themes. It then analyzes their temporal evolution across the Martian solid, surface, atmosphere, and space environments. Finally, through sentiment analysis and semantic matching, it highlights unresolved scientific questions and potential directions for future research. This approach offers a novel perspective on the knowledge structure underlying Mars exploration and demonstrates the potential of NLP for large-scale literature analysis in planetary science. The findings potentially provide a structured foundation for building an interdisciplinary, peer-reviewed Mars knowledge base, which may inform future scientific research and mission planning.