| Natural language processing (NLP) techniques, as a suite of computational technologies centered on language, are increasingly gaining the attention of researchers in the humanities and social sciences due to their powerful capabilities in processing large-scale textual data. These techniques enable automatic text classification, label extraction, and the efficient analysis of vast text corpora. Researchers in higher education are tasked with extracting and analyzing data from complex and expansive databases, as well as periodically re-evaluating existing knowledge and the processes of knowledge production. NLP techniques demonstrate significant potential in assisting and enhancing these research efforts. This study reviewed the applications of various NLP techniques in text analysis, explored cutting-edge research in this area, and examined how NLP can support higher education research. It also outlined the further directions for innovating the methods employed by higher education researchers. Current higher education research incorporating NLP techniques has demonstrated notable advancements in the depth, accuracy, and reliability of data analysis. NLP technology plays a critical role in enhancing the efficiency of research processes, deepening the exploration of topics, expanding theoretical and practical boundaries, and contributing to the construction of autonomous knowledge systems. These advancements underscore the meaningful and valuable contributions of NLP to the evolving landscape of higher education research. |
