Journal ArticleDOI
A decade of research in statistics: a topic model approach
TLDR
In this paper, a methodological approach to topic modeling and post-processing of topic models results to the end of describing in depth a field of research over time, in particular, a selection of publications from the international statistical literature, and analyze the links between topics and their temporal evolution.Abstract:
Topic models are a well known clustering approach for textual data, which provides promising applications in the bibliometric context for the purpose of discovering scientific topics and trends in a corpus of scientific publications. However, topic models per se provide poorly descriptive metadata featuring the discovered clusters of publications and they are not related to the other important metadata usually available with publications, such as authors affiliation, publication venue, and publication year. In this paper, we propose a methodological approach to topic modeling and post-processing of topic models results to the end of describing in depth a field of research over time. In particular, we work on a selection of publications from the international statistical literature, we propose an approach that allows us to identify sophisticated topic descriptors, and we analyze the links between topics and their temporal evolution.read more
Citations
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Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods
TL;DR: Two alternative methods for retaining keywords that match expert selection much better and reveal the research specialization of the domain with more details are introduced: TF-inverse document frequency (TF-IDF) and TF-Keyword Activity Index (TF -KAI).
Journal ArticleDOI
Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014
TL;DR: This research proposes a topic-based technological forecasting approach, to uncover the trends of specific topics underlying massive patent claims using topic modelling, and indicates that the proposed approach is effective for estimating the temporal patterns and forecast the future trends of the latent topics underlyingmassive claims.
Journal ArticleDOI
Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016
TL;DR: A topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016 is conducted and indicates that the interest of K noSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized.
Journal ArticleDOI
Identifying core topics in technology and innovation management studies: a topic model approach
Hakyeon Lee,Pilsung Kang +1 more
TL;DR: This study adopts the topic model approach, which automatically discovers topics that pervade a large and unstructured collection of documents, to uncover research topics in TIM research.
Journal ArticleDOI
Assessment of online public opinions on large infrastructure projects: A case study of the Three Gorges Project in China
TL;DR: An assessment framework to transform unstructured online public opinions on large infrastructure projects into sentimental and topical indicators for enhancing practices of ex post evaluation and public participation is proposed and investigated on China's largest microblogging site, namely, Weibo.
References
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Book ChapterDOI
Probabilistic Topic Models
TL;DR: Landauer and Dumais as discussed by the authors showed that applying a statistical method such as latent semantic analysis (LSA) to large databases can yield insight into human cognition, and proposed a class of statistical models in which the semantic properties of words and documents are expressed in terms of probabilistic topics.