Design and Development of Topic Modeling for Probabilistic Recurrent Neural Network
P. Lakshmi Prasanna1, D. Rajeswara Rao2
1P.Lakshmi Prasanna, Research Scholar, Computer science and Engineering, KL University, Vijayawada (Andhra Pradesh), India.
2Dr. D. Rajeswara Rao, Professor, Computer science and Engineering, KL University, Vijayawada (Andhra Pradesh), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 1062-1069 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7113068519/19©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: A topic model is a probabilistic model that discovers the main themes in a collection of documents. The basic idea is to treat the documents as mixtures of topics in the topic model, and each topic is viewed as a probability distribution of the words. In this paper we proposed LDA Algorithm and Probabilistic recurrent neural network algorithm (PRORNN) to classify the text documents. Topic modeling refers to the task of Discovering Latent Topics in the text corpus set, where the output is commonly represented as top terms appearing in each topic. Out algorithm is implemented by taking 20 news group data set and all the results related to LDA algorithm and probabilistic recurrent neural network (PRORNN) are tabulated. We compared our model with the state of art of algorithms of text classification.
Keywords: Text, Topics, Neural Network, Drichlent Allocation, Corpus.
Scope of the Article: Computer Network