A Review on the Ensemble Framework for Sentiment Analysis
Lai Po Hung, Rayner Alfred, Mohd Hanafi Ahmad Hijazi, Ag Asri Ag Ibrahim · Advanced Science Letters · 2015
Sentiment analysis is an important task for the automated classification of positive and negative opinions by a machine. The approaches to this task can either be rule-based or machine learning, the later being the current trend due to its automaticity and versatility. An ensemble framework based on machine learning classifiers diversifies the text data and learning process in order to produce accurate sentiment classification. In this review paper, we highlight the various parts to consider when creating an ensemble of machine learning classifiers for sentiment analysis. Starting from the selection of suitable text features until the training of the machine learner, they all influence the accuracy of the system. The ensemble framework improves the accuracy of the system based on the concept that stronger classifiers compensate for the performance of weak classifiers. Concluding thought to the paper is the inclusion of diversified feature selection method into the ensemble framework to select useful features and reduce size of feature set.