Extended correlated principal component analysis with SVM-PUK in opinion mining

Authors : Kollimarla Anusha DEVI, Deepak Chowdary EDARA, Venkatrama Phani Kumar SISTLA, Venkata Krishna Kishore KOLLI

Pages : 2570-2582

View : 14 | Download : 6

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :With the rapid growth of microblogs and online sites, an inordinate number of product reviews are available on the Internet. They not only help in analyzing, but also assist in making informed decisions about product quality. In the proposed work, an extended correlated principal component analysis insert ignore into journalissuearticles values(ECPCA); is used for dimensionality reduction. A comparative analysis is conducted on movie reviews insert ignore into journalissuearticles values(DB-1); and Twitter datasets insert ignore into journalissuearticles values(DB-2 and DB-3); in opinion mining extraction. The performance of naive Bayes, CHIRP, and support vector machine insert ignore into journalissuearticles values(SVM); with kernel methods such as radial basis function insert ignore into journalissuearticles values(RBF);, polynomial, and Pearson insert ignore into journalissuearticles values(PUK); are compared and analyzed on the three datasets. The experimental results using ECPCA for selecting relevant features and SVM-PUK as a classifier exhibit better performance on movie reviews and Twitter datasets. The performance of the proposed approach is 99.69 %, 99.4 %, and 99.54 % on the DB-1, DB-2, and DB-3 datasets, respectively, and comparatively outperforms the existing methods.
Keywords : Opinion mining, latent Dirichlet allocation, principal component analysis, dimensionality reduction, support vector machine