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Intelligent Computing - Proceedings of the Computing Conference

CompCom 2019: Intelligent Computing pp 573–592Cite as

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

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Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, College of Electrical & Mechanical Engineering (E&ME), National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Muhammad Latif & Usman Qamar

  2. National Centre for Big Data and Cloud Computing (NCBC), Lahore, Pakistan

    Muhammad Latif & Usman Qamar

Authors
  1. Muhammad Latif
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  2. Usman Qamar
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Corresponding author

Correspondence to Usman Qamar .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information SAI Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information SAI Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Latif, M., Qamar, U. (2019). A Novel Ensemble Approach for Feature Selection to Improve and Simplify the Sentimental Analysis. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-22871-2_39

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