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Dynamic topic detection and tracking: A comparison of HDP , C‐word, and cocitation methods
Author(s) -
Ding Wanying,
Chen Chaomei
Publication year - 2014
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23134
Subject(s) - computer science , probabilistic logic , generative grammar , word (group theory) , tracking (education) , enhanced data rates for gsm evolution , generative model , artificial intelligence , information retrieval , data mining , data science , linguistics , psychology , pedagogy , philosophy
Cocitation and co‐word methods have long been used to detect and track emerging topics in scientific literature, but both have weaknesses. Recently, while many researchers have adopted generative probabilistic models for topic detection and tracking, few have compared generative probabilistic models with traditional cocitation and co‐word methods in terms of their overall performance. In this article, we compare the performance of hierarchical D irichlet process ( HDP ), a promising generative probabilistic model, with that of the 2 traditional topic detecting and tracking methods—cocitation analysis and co‐word analysis. We visualize and explore the relationships between topics identified by the 3 methods in hierarchical edge bundling graphs and time flow graphs. Our result shows that HDP is more sensitive and reliable than the other 2 methods in both detecting and tracking emerging topics. Furthermore, we demonstrate the important topics and topic evolution trends in the literature of terrorism research with the HDP method.