An Efficient Text Analysis Tool for Streamlining Literature Reviews

For emerging scientific investigators in STEM and other areas, one of the initial challenges in preparing a research project is to identify the knowledge gap. Before starting the actual work, it is essential to understand this gap in the context of existing research. Conducting a literature review, however, can be overwhelming. Researchers often rely on keyword-based search algorithms that return noisy results, making it difficult to find relevant articles and to note the connections that the articles share. To address this challenge, we present BeagleTA, a new tool designed to streamline the knowledge exploration process. This open-source tool, available on GitHub (URL; https://github.com/developmentAC/BeagleTA), uses command-line interfaces for integration with various workflows, offering fast and memory-efficient performance. %Written in Rust, our proposed tool provides statistical and visual results to help researchers identify meaningful keywords and articles more intuitively, in addition to determining relationships to the project. Using a bag-of-words approach, the tool operates locally with a user-maintained corpus and applies keywords across the documents of the corpus. The tool generates interactive visualizations such as statistics, plots, heatmaps, and networks to explore relationships between articles and projects. The tool offers customization filters, including article time-frames, and keyword sensitivity analysis, serving to refine results and add performance for successful literature reviews. This tool aims to empower students and emerging researchers by providing visual representations of knowledge gaps, improving comprehension, and reducing frustrations when building literature reviews.