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A Novel Ensemble Approach for Feature Selection to Improve and Simplify the Sentimental Analysis

  • Muhammad Latif
  • Usman QamarEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 997)

Abstract

Text Classification is a renowned machine learning approach to simplify the domain-specific investigation. Consequently, it is frequently utilized in the field of sentimental analysis. The demanding business requirements urge to devise new techniques and approaches to improve the performance of sentimental analysis. In this context, ensemble of classifiers is one of the promising approach to improve classification accuracy. However, classifier ensemble is usually done for classification while ignoring the significance of feature selection. In the presence of right feature selection methodology, the classification accuracy can be significantly improved even when the classification is performed through a single classifier. This article presents a novel feature selection ensemble approach for sentimental classification. Firstly, the combination of three well-known features (i.e. lexicon, phrases and unigram) is introduced. Secondly, two level ensemble is proposed for feature selection by exploiting Gini Index (GI), Information Gain (IG), Support Vector Machine (SVM) and Logistic Regression (LR). Subsequently, the classification is performed through SVM classifier. The implementation of proposed approach is carried out in GATE and RapidMiner tools. Furthermore, two benchmark datasets, frequently utilized in the domain of sentimental classification, are used for experimental evaluation. The experimental results prove that our proposed ensemble approach significantly improve the performance of sentimental classification with respect to well-known state-of-the-art approaches. Furthermore, it is also analyzed that the ensemble of classifiers for the improvement of classification accuracy is not necessarily important in the presence of right feature selection methodology.

Keywords

Feature selection ensemble Classifiers ensemble Sentimental classification SVM 

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, College of Electrical & Mechanical Engineering (E&ME)National University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.National Centre for Big Data and Cloud Computing (NCBC)LahorePakistan

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