Topic Mining and Future Trend Exploration in Digital Economy Research

Abstract

This work proposes a new literature topic clustering analysis framework, based on which the topics of digital-economy-related studies are condensed. First, we calculated the word vector of keywords using the FastText model, and then the keywords were merged according to semantic similarity. A hierarchical clustering method based on the Jaccard coefficient was employed to cluster the domain documents. Finally, the information gain method was applied to estimate the high-gain feature words for each category of topics. Based on the above framework, 23 categories of research topics were formed. We divided these topics into layers of digital technology, convergence innovation and digital governance, and we constructed a three-level digital economy research framework. Thereafter, the current hot spots and frontier trends were derived based on the number and growth rate of the literature. Our study revealed that the research on digital technology, which is the basic layer of the digital economy, has waned. The field related to the integration and innovation of digital technology and the real economy was the current research focus, among which the results with respect to “New Business Forms in the Digital Age”, “Circular Economy” and “Gig Economy” were abundant. The problems of the unbalanced development of the digital economy and digital monopoly have strengthened research on digital governance. Furthermore, research on “Regional Digital Economy”, “Chinese Digital Economy” and “Data Management” is in its initial stage and is a potential area of future research.

Hierarchical Keyword Generation Method for Low-Resource Social Media Text

“…Social media has emerged as a prominent platform for online social interaction and sharing opinions, which has led to an overwhelming volume of text information, leading to the problem of social media text information overload. The keyword generation technology in natural language processing (NLP) can automatically extract text features and generate the central words or phrases that best reflect the theme of the text, which not only helps us quickly acquire essential information and better understand the content, but also can be applied to various downstream NLP tasks, such as document classification [1,2], recommendation systems [3,4], information retrieval [5,6], text summarization [7,8], text classification [9], and knowledge graph [10]. Therefore, it is a vital method to alleviate the problem of text information overload.…”