Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application

Abstract

Objectives: The main objective of this study is to compare the performance evaluation of ensemble based methods and neural network learning on various combinations of unigram, bigram, and trigram feature vector along with feature selection (IG) and feature reduction (PCA) for sentiment classification of movie reviews. Methods: Bagging and Adaboost are the techniques used in ensemble learning to learn the sentiment classifier to get better classification accuracy, using SVM, NB as a core learner for different models of attribute vectors. The classification results of the ensemble approach are compared with neural network learning for classification of movie reviews. Among the ensemble methods, AdaBoost with base learner SVM outperforms in classifying attribute vectors for model m-iii. The backpropagation algorithm is used to improve classification accuracy in the neural network learning and IG and PCA are used in sentiment classification to reduce the feature length and training time. Findings:The classification results of ensemble based approach are compared with neural network learning. Between the two ensemble based methods, Adaboost + SVM outperform in classifying the sentiment of movie reviews for m-iii feature vector. IG and PCA are used in sentiment classification in order to reduce the feature length. Between the IG and PCA methods, IG performs better than PCA. Among IG+Adaboost+SVM and neural network learning methods, IG+Adaboost+SVM performs better than neural network learning. Improvement: In our application, we are using the ensemble based methods and neural network learning, these methods are compared and analyzed the performance for various levels of feature vectors. A classification algorithm may be designed to analyze the performance with other neural network methods.