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.
| Published in | American Journal of Mathematical and Computer Modelling (Volume 2, Issue 3) |
| DOI | 10.11648/j.ajmcm.20170203.12 |
| Page(s) | 84-87 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Topic Models, Topic Modelling Methods, LSA, PLSA, LDA, CTM, Tools
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APA Style
Himanshu Sharma, Arvind K. Sharma. (2017). Study and Analysis of Topic Modelling Methods and Tools – A Survey. American Journal of Mathematical and Computer Modelling, 2(3), 84-87. https://doi.org/10.11648/j.ajmcm.20170203.12
ACS Style
Himanshu Sharma; Arvind K. Sharma. Study and Analysis of Topic Modelling Methods and Tools – A Survey. Am. J. Math. Comput. Model. 2017, 2(3), 84-87. doi: 10.11648/j.ajmcm.20170203.12
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Download@article{10.11648/j.ajmcm.20170203.12,
author = {Himanshu Sharma and Arvind K. Sharma},
title = {Study and Analysis of Topic Modelling Methods and Tools – A Survey},
journal = {American Journal of Mathematical and Computer Modelling},
volume = {2},
number = {3},
pages = {84-87},
doi = {10.11648/j.ajmcm.20170203.12},
url = {https://doi.org/10.11648/j.ajmcm.20170203.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20170203.12},
abstract = {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.},
year = {2017}
}
TY - JOUR T1 - Study and Analysis of Topic Modelling Methods and Tools – A Survey AU - Himanshu Sharma AU - Arvind K. Sharma Y1 - 2017/03/09 PY - 2017 N1 - https://doi.org/10.11648/j.ajmcm.20170203.12 DO - 10.11648/j.ajmcm.20170203.12 T2 - American Journal of Mathematical and Computer Modelling JF - American Journal of Mathematical and Computer Modelling JO - American Journal of Mathematical and Computer Modelling SP - 84 EP - 87 PB - Science Publishing Group SN - 2578-8280 UR - https://doi.org/10.11648/j.ajmcm.20170203.12 AB - 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. VL - 2 IS - 3 ER -
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