A Text Mining Research Based on Topic Modeling using Latent Dritchlent Allocation
P. Lakshmi Prasanna1, D. Rajeswara Rao2
1P. Lakshmi Prasanna, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, (Andhra Pradesh), India – 522502.
2Dr. D. Rajeswara Rao, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, (Andhra Pradesh), India – 522502.
Manuscript received on 24 January 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 January 2019 | PP: 308-317 | Volume-7 Issue-6, March 2019 | Retrieval Number: E2036017519©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Topic modelling is started from text-mining technique for discovering the latent semantic structure in a collection of documents. In the concept of text mining each document is generated from collection of topics. Topic modelling is based on probabilistic modeling, it has a huge range of applications such as linguistic understanding, image detection, automatic music improvisation identification etc.. topic modeling is applied in various fields such as software engineering, political science, medical etc.In this paper we propose topic modelling using LDA (Latent Dirichelt Allocation).LDA is one kind of probabilistic model that work backwards to learn the topic representation in each document and the word distribution of each topic. this paper I will focusing on LDA algorithms and the results shown based on the 20 news group data set. I will also show how topic modelling works on news groups data set on R Tool. Topic Models to analysis news groups data set with tm and topic modelling package in R, to see what are those documents from different topics.
Keywords: Topic Modeling, Text, Corpus, LDA, LSA, Gibbs Sampling.
Scope of the Article: Emergent Topics