A Python Framework for Big Data Text Analytics
Lina Zhao, Xinyun Zhang · 2025
Various disciplines are exploring effective text big data mining and analysis techniques. Due to its traditional emphasis on theory, the field of social sciences is in a relatively weak position in the mining and analysis of text big data, and there is a need to continuously enrich the logical paradigm of text data mining and analysis from multiple perspectives. This study takes the analysis of protective factors for the resilience of veterans as an example. Based on the mining and analysis of 353 items and 1,214,173 Chinese characters, a complete text data mining and analysis program based on Python tools and assisted by web crawling tools is presented to construct a universally practical text data mining and analysis model in the field of social sciences. The research includes four parts: the research design part, which explains the crawling method of text materials, the basis for data filtering, and the applicability of Python; The analysis logical construction section, discusses the overall logical structure of the research, and constructs the logic for running Python programs; The text mining and analysis results section, presents the structure and priority of protective factors for veterans' resilience under big data; In the conclusion section, taking the research topic as an example, summarize the advantages and disadvantages of text big data mining and analysis, and identify areas for improvement and enhancement in the future. Research innovatively provides a reference paradigm for text big data mining and analysis in the field of social sciences.