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Reseach Article

Sentiment Classification based on Latent Dirichlet Allocation

Published on July 2015 by Raja Mohana S.p, Umamaheshwari K., Karthiga R.
International Conference on Innovations in Computing Techniques (ICICT 2015)
Foundation of Computer Science USA
ICICT2015 - Number 2
July 2015
Authors: Raja Mohana S.p, Umamaheshwari K., Karthiga R.
4a43050e-f059-469d-9f21-92eaaf43c4a4

Raja Mohana S.p, Umamaheshwari K., Karthiga R. . Sentiment Classification based on Latent Dirichlet Allocation. International Conference on Innovations in Computing Techniques (ICICT 2015). ICICT2015, 2 (July 2015), 14-16.

@article{
author = { Raja Mohana S.p, Umamaheshwari K., Karthiga R. },
title = { Sentiment Classification based on Latent Dirichlet Allocation },
journal = { International Conference on Innovations in Computing Techniques (ICICT 2015) },
issue_date = { July 2015 },
volume = { ICICT2015 },
number = { 2 },
month = { July },
year = { 2015 },
issn = 0975-8887,
pages = { 14-16 },
numpages = 3,
url = { /proceedings/icict2015/number2/21462-1479/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Innovations in Computing Techniques (ICICT 2015)
%A Raja Mohana S.p
%A Umamaheshwari K.
%A Karthiga R.
%T Sentiment Classification based on Latent Dirichlet Allocation
%J International Conference on Innovations in Computing Techniques (ICICT 2015)
%@ 0975-8887
%V ICICT2015
%N 2
%P 14-16
%D 2015
%I International Journal of Computer Applications
Abstract

Opinion miningrefers to the use of natural language processing, text analysis and computational linguistics to identify and extract the subjective information. Opinion Mining has become an indispensible part of online reviews which is in the present scenario. In the field of information retrieval, a various kinds of probabilistic topic modeling techniques have been used to analyze contents present in a document. A topic model is a generative technique for document. All topic models share the idea that documents are having mixture of topics, and the topic is a probability distribution over words. Recently topic modeling techniques have been used to identify the meaningful review aspects, but existing topic models like Latent Dirichlet Markov Allocation (LDMA), hierarchical aspect sentiment model (HASM) do not identify aspect specific opinion words and also not suitable for shared features. In the proposed system, movie review dataset is collected from the IMDB database and is preprocessed. TF-IDF is calculated for the preprocessed data and result is given to LDA model which is then used to discover both the aspects and aspect specific opinion words. After that CHI value has been determined, SVM classifier is used to classify the topics preferable to each and every document.

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Index Terms

Computer Science
Information Sciences

Keywords

Latent Dirichletallocation Support Vector Machine Tf-idf Chi Value.