A review on the ensemble framework for sentiment analysis

Hung, Lai Po and Rayner Alfred, and Mohd Hanafi Ahmad Hijazi, and Ag. Asri Ag. Ibrahim, (2015) A review on the ensemble framework for sentiment analysis. Advanced Science Letters, 21 (10). pp. 2957-2962.

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Ensemble; Feature Extraction; Feature Selection; Machine Learning; Sentiment Analysis
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: FACULTY > Faculty of Engineering
Depositing User: Unnamed user with email storage.bpmlib@ums.edu.my
Date Deposited: 08 May 2017 07:16
Last Modified: 25 Oct 2017 06:09
URI: http://eprints.ums.edu.my/id/eprint/15366

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