TopicTracker - An advanced software pipeline for text mining on PubMed data: Bridging the gap between off-the-shelf...

Area of Science:

• Bibliometrics
• Computational Linguistics
• Information Science

Background:

• The exponential growth of academic publications presents challenges for researchers in identifying trends and synthesizing information.
• Traditional literature search methods often lack the sophistication to capture complex relationships within research domains.

Purpose of the Study:

• To introduce TopicTracker, a novel software tool designed for advanced text mining and literature analysis.
• To demonstrate TopicTracker's capability in systematically searching databases, analyzing trends, and visualizing semantic networks.

Main Methods:

• TopicTracker queries PubMed databases to retrieve relevant literature.
• The software generates semantic network maps to visualize co-occurrence patterns.
• Case studies illustrate the tool's application in areas like infodemic management and research evolution.

Main Results:

• TopicTracker offers a comprehensive solution for querying, analyzing, and visualizing academic literature.
• Case studies demonstrate the tool's precision in mapping research trends, such as ethical considerations in public health.
• The software facilitates the identification of interconnections within large datasets.

Conclusions:

• TopicTracker represents a significant advancement in text mining tools for academic research.
• Despite limitations related to database alignment, its benefits in navigating complex literature are substantial.
• The tool can guide researchers in efficiently pursuing knowledge and maintaining clarity in their studies.