# Sentence-Level Insights from the Martian Literature: A Natural Language Processing Approach
## 1 Introduction  
Mars, as one of the most closely studied terrestrial planets in the solar system, occupies a central position in planetary science due to evidence of past water activity, potential signs of life, and its geological evolution [1, 2]. Research on Mars not only advances our understanding of Earth's history and future but also holds strategic importance for prospective interstellar migration and resource utilization. As noted in [3], \" Mars exploration is a topic that concerns the fate of human beings and influences the lasting of human civilization,\" and has therefore attracted growing scientific attention.  
Over the past two decades, the number and accuracy of Mars exploration missions have steadily increased. As a result, Mars research has entered a data-intensive period [4], enabling a deeper understanding of key scientific topics such as geological processes, climate evolution, and the potential for life. The objectives of Mars missions have expanded beyond terrain and geomorphological studies to include the search for signs of life, reconstruction of climate history, assessment of habitability, and preparation for sample return and future human exploration. These aims have driven the deployment of various orbiters, landers, rovers, and sample return missions, supporting systematic investigations of Martian geology, climate, magnetic fields, and radiation environment [5; 6; 7].  
With the rapid growth in the Martian literature, along with the increasing interdisciplinarity of research topics, traditional manual reviews are showing significant limitations in large-scale knowledge integration. These methods, which typically rely on manual screening and subjective judgment, often lack systematic structure and reproducibility, making it difficult to effectively capture semantic relationships and temporal trends across research areas [8]. Meanwhile, the continuous gain of heterogeneous scientific data from Mars missions presents researchers with the dual challenges of information overload and the need for efficient data organization and interpretation [4]. In this highly interdisciplinary and data-intensive research environment, the adoption of automated and structured text mining and semantic analysis methods has become essential for advancing literature analysis and scientific understanding.  
While natural language techniques and systematic reviews are increasingly used in ecology and environmental science [9], such approaches remain rare in planetary science. Prior meta-analyses often rely on metadata or abstracts from databases like Web of Science. For instance, a recent review of Google Earth Engine applications conducted a large-scale bibliometric and keyword-based analysis, rather than applying sentence-level semantic techniques [10].  
In this work, we (1) develop a framework for sentence-level understanding and structured processing of the interdisciplinary Martian research literature using natural language processing (NLP) techniques; (2) identify thematic evolution, sentiment trends, and hotspot distributions in Martian research across domains from the solid planet to outer space; and (3) explore unresolved scientific questions, technological bottlenecks, and future research directions, providing insights into planetary science research and space mission planning. This study aims to support the systematic organization and interpretation of domain knowledge in planetary science, offering structured insights into research.