Topic Modelling: A Comparative Study |
Author(s): |
| Shraddha Anant Narhari , Ramrao Adik Institute of Technology; Rajashree Shedge, Ramrao Adik Institute of Technology |
Keywords: |
| Natural Language Processing, Topic Modelling, Latent Dirichlet Allocation |
Abstract |
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Topic modeling is useful in the areas like of text categorization, text mining, machine learning etc. It focused on creating statistical models to classify many topics in a collection of documents. Topic models provide a simple way to analyze large volumes of unlabelled text. It is required because there is huge amount of data present in different documents, online websites and social media like twitter, Wikipedia etc. we should know what the document is talking about to find out its the importance. A topic consists of a cluster of words that frequently occur together. Topic modeling is a technique used for discovering the latent topics that are hidden in a collection of documents, which will help for text categorization and opinion mining. This paper covers study various techniques used for topic modeling and gives comparative analysis of them on the basis of different parameters. |
Other Details |
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Paper ID: IJSRDV5I80241 Published in: Volume : 5, Issue : 8 Publication Date: 01/11/2017 Page(s): 218-220 |
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