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

Cited 74 time in Web of Science Cited 99 time in Scopus
Authors

Kim, Suhyeon; Park, Haecheong; Lee, Junghye

Issue Date
2020-08
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS, Vol.152
Abstract
Blockchain has become one of the core technologies in Industry 4.0. To help decision-makers establish action plans based on blockchain, it is an urgent task to analyze trends in blockchain technology. However, most of existing studies on blockchain trend analysis are based on effort demanding full-text investigation or traditional bibliometric methods whose study scope is limited to a frequency-based statistical analysis. Therefore, in this paper, we propose a new topic modeling method called Word2vec-based Latent Semantic Analysis (W2V-LSA), which is based on Word2vec and Spherical k-means clustering to better capture and represent the context of a corpus. We then used W2V-LSA to perform an annual trend analysis of blockchain research by country and time for 231 abstracts of blockchain-related papers published over the past five years. The performance of the proposed algorithm was compared to Probabilistic LSA, one of the common topic modeling techniques. The experimental results confirmed the usefulness of W2V-LSA in terms of the accuracy and diversity of topics by quantitative and qualitative evaluation. The proposed method can be a competitive alternative for better topic modeling to provide direction for future research in technology trend analysis and it is applicable to various expert systems related to text mining. (C) 2020 The Authors. Published by Elsevier Ltd.
ISSN
0957-4174
URI
https://hdl.handle.net/10371/200488
DOI
https://doi.org/10.1016/j.eswa.2020.113401
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  • Graduate School of Engineering Practice
  • Department of Engineering Practice
Research Area Deep Learning, Machine Learning, Privacy-preserving Federated Learning, Smart Healthcare

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