Management Science Hotspots Identification And Evolution Analysis Based On Text Mining

Computer science has entered the stage of development across the era.The maturity of artificial intelligence and text mining technology provides new means and methods for in-depth analysis of the long texts of scientific research literature.At present,the LDA topic model has shown excellent performance in microblog topic recognition,news topic recognition and other fields,but it has few applications in the field of scientific research literature.Some scholars have applied LDA topic models to the fields of Information science,computer science and so on.Academic journals conduct topical analysis.The application of deep semantic mining model in China is still in its infancy in the scientific literature research in the field of management science.This paper takes the lead in applying the LDA topic model to the topic mining of academic literature in the field of management science,and then explores the evolution and development of the subject of management science in China.This paper analyzes the abstract part of the data selection journal.The abstract is a high-level summary and summary of the paper by scholars,covering the overall information of the literature.Compared with the general keyword co-occurrence analysis method,the subject extraction of the long text of the abstract can retain the original information of the literature to a greater extent,and can solve the problem that the keyword cannot summarize the literature information to a certain extent.This paper innovatively proposes a probability threshold setting based on first-order difference and a data segmentation method based on sliding time window.The differential probability threshold setting effectively improves the problem of difficult topic screening,effectively identifies topic groups with similar probabilities,and improves the topic recognition process.The data segmentation of the sliding time window proposed in this paper overcomes the difficulty of topic alignment in different time regions and improves the topic fault problem.This paper completes the topic recognition work on the whole data through the LDA topic model,and builds the model based on the data partition of the sliding time window to realize the evolution analysis of the hot topics,including the evolution of the topic intensity and the evolution of the topic content.The quantitative model of thehotspot analysis of scientific papers in the field of management science through the LDA theme model is helpful to discover the frontier issues in the field of management science in China,explore the evolution direction of the discipline of management science in China,and then be able to identify the key areas of future research in management science in China.The macroscopic overall perception of management science in China.