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Study and Analysis of Topic Modelling Methods and Tools – A Survey
American Journal of Mathematical and Computer Modelling
Volume 2, Issue 3, August 2017, Pages: 84-87
Received: Jan. 30, 2017; Accepted: Feb. 18, 2017; Published: Mar. 9, 2017
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Himanshu Sharma, School of CSE, Jaipur National University, Jaipur, India
Arvind K. Sharma, Dept of CSI, University of Kota, Kota, Rajasthan, India
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Now days, topic models have been widely used to identify topics in text corpora. Topic modelling is a mechanism of extracting common topics which occurs among the collection of documents. Topic models are actually a suite of algorithms which uncover the hidden thematic structure in document collections. These algorithms shall definitely be help to develop new paradigms to search, browse and summarize large archive of texts. This paper presents a survey of various important topic modelling techniques and tools which highlights the probabilistic topic models. The primary aim of this paper is to help researchers who do not have a strong background in mathematics or statistics to feel comfortable with using topic modelling methods and tools in their research work. Apart from it, the merits and demerits of topic modelling methods are also summarized.
Topic Models, Topic Modelling Methods, LSA, PLSA, LDA, CTM, Tools
To cite this article
Himanshu Sharma, Arvind K. Sharma, Study and Analysis of Topic Modelling Methods and Tools – A Survey, American Journal of Mathematical and Computer Modelling. Vol. 2, No. 3, 2017, pp. 84-87. doi: 10.11648/j.ajmcm.20170203.12
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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