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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (11): 64-72    DOI: 10.11925/infotech.2096-3467.2018.0292
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Analyzing Scientific Literature with Content Similarity - Topics over Time Model
Weilin He(),Guohe Feng,Hongling Xie
School of Economics & Management, South China Normal University, Guangzhou 510006, China
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[Objective] This paper studies the topics of scientific literature and then tracks their changes.[Methods] We used the improved CSToT Model (Content Similarity - Topics over Time), to analyze scholarly papers from 9 information science journals in China published from 2012-2016. [Results] The CSToT model effectively revealed the subject structure of scientific literature and the evolution of topics. We also found that majority of the current information science research covers information services, online public opinion and data mining. Their evolution trends include rising, falling, stable and fluctuating patterns, which are particularly prominent in information services research. [Limitations] The training data set needs to be expanded. [Conclusions] The CSToT model could effectively identify the topics of scientific literature and their evolutionary trends, which provide new directions for future research.

Key wordsTopics over Time Topic Model      Topic Extraction      Topic Evolution     
Received: 16 March 2018      Published: 11 December 2018

Cite this article:

Weilin He,Guohe Feng,Hongling Xie. Analyzing Scientific Literature with Content Similarity - Topics over Time Model. Data Analysis and Knowledge Discovery, 2018, 2(11): 64-72.

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