Identification Of Domain Development Trajectory Based On Topic Evolution

With the rapid development of emerging areas such as mobile internet,cloud computing and blockchain,people's lifestyles have undergone profound changes.In order to be able to predict the development of the field and take effective countermeasures to seize the opportunity,relevant politicians,business people,and scholars need to have sufficient knowledge and understanding of the development process of the field.Therefore,it is urgent to quickly identify evolutionary paths in a specific field.To solve the problem,this article has carried out the following work:The article first investigated the characteristics of the evolution path identification method in the previous field,and found that the traditional method has many limitations,making it difficult to adapt to the current situation of the explosive growth of the literature.This article next attempts to use text data mining methods for research.However,most of the current thematic change methods cannot simultaneously consider the changes of the subject content and the changes of the subject-related scholars.Therefore,this paper proposes new methods and processes to solve this problem.Then it proposes a domain evolution path identification method based on topic transition.The model can automatically obtain data from Aminer platform.Through mining the semantic information hidden in scientific literature,the research topics in different time periods can be obtained.The Jaccard similarity is used to calculate the correlation between different topics,to obtain evolutionary paths and to visualize them.Through empirical analysis of the field of artificial intelligence,the results show that the model can effectively reflect the changes in the field of research topics.Finally,we conducted in-depth research on domain topic mining.In order to solve the problem that the number of topics in the traditional topic model needs to be predefined and lack of external information,a hierarchical Dirichlet process(ciHDP)for merging citation information is proposed.In order to verify the validity of the algorithm,the author used the data of Cora and Krypton as the experimental data set to carry out quantitative comparative experiments.At the same time,qualitative analysis was performed using Aminer data.The results of quantitative and qualitative analysis jointly demonstrated the improved method in topic mining.Analytical validity.