Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec

The combination of the topic model and the semantic method can help to discover the semantic distributions of topics and the changing characteristics of the semantic distributions, further providing a new perspective for the research of topic evolution. This study proposes a solution for quantifying the semantic distributions and the changing characteristics based on words in topic evolution through the Dynamic topic model (DTM) and the word2vec model. A dataset in the field of Library and information science (LIS) is utilized in the empirical study, and the topic-semantic probability distribution is derived. The evolving dynamics of the topics are constructed. The characteristics of evolving dynamics are used to explain the semantic distributions of topics in topic evolution. Then, the regularities of evolving dynamics are summarized to explain the changing characteristics of semantic distributions in topic evolution. Results show that no topic is distributed in a single semantic concept, and most topics correspond to various semantic concepts in LIS. The three kinds of topics in LIS are the convergent, diffusive, and stable topics. The discovery of different modes of topic evolution can further prove the development of the field. In addition, findings indicate that the popularity of topics and the characteristics of evolving dynamics of topics are irrelevant.

Automated summary

The study proposes a solution for quantifying the semantic distributions and changing characteristics of topics in topic evolution using the Dynamic topic model (DTM) and the word2vec model. Results show that topics in the field of Library and information science (LIS) correspond to various semantic concepts and exhibit three types of evolution: convergent, diffusive, and stable. Additionally, the popularity of topics does not affect the characteristics of their evolving dynamics.