Open AccessText Classification of Cornell Movie Data using Data Mining with Feature Selection
Author(s) -
Akhilesh Kumar Shrivas,
Shreya Ghosh,
Amit Kumar Dewangan
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b2329.129219
Subject(s) - naive bayes classifier , feature selection , support vector machine , artificial intelligence , computer science , preprocessor , random forest , data pre processing , pattern recognition (psychology) , classifier (uml) , data mining , perceptron , data set , machine learning , artificial neural network
Text Classification is branch of text mining through which we can analyze the sentiment of the movie data. In this research paper we have applied different preprocessing techniques to reduce the features from cornell movie data set. We have also applied the Correlation-based feature subset selection and chi-square feature selection technique for gathering most valuable words of each category in text mining processes. The new cornell movie data set formed after applying the preprocessing steps and feature selection techniques. We have classified the cornell movie data as positive or negative using various classifiers like Support Vector Machine (SVM), Multilayer Perceptron (MLP), Naive Bayes (NB), Bays Net (BN) and Random Forest (RF) classifier. We have also compared the classification accuracy among classifiers and achieved better accuracy i. e. 87% in case of SVM classifier with reduced number of features. The suggested classifier can be useful in opinion of movie review, analysis of any blog and documents etc.