Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis

作者：

Highlights：

• Blockchain has a considerable value as one of the promising technologies in industrial 4.0.

• We propose a new topic modeling method based on word embedding and clustering.

• The proposed method outperforms an existing method in both qualitative and quantitative views.

• The proposed method contributes to analyzing the research trend of blockchain technology.

摘要

•Blockchain has a considerable value as one of the promising technologies in industrial 4.0.•We propose a new topic modeling method based on word embedding and clustering.•The proposed method outperforms an existing method in both qualitative and quantitative views.•The proposed method contributes to analyzing the research trend of blockchain technology.

论文关键词：Trend analysis,Topic modeling,Word2vec,Probabilistic latent semantic analysis,Blockchain

论文评审过程：Received 24 November 2019, Revised 3 February 2020, Accepted 20 March 2020, Available online 1 April 2020, Version of Record 27 April 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113401