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Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)

Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)

Parag Verma, Ankur Dumka, Anuj Bhardwaj, Alaknanda Ashok
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 21
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.2022010107
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MLA

Verma, Parag, et al. "Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)." IJAMC vol.13, no.1 2022: pp.1-21. http://doi.org/10.4018/IJAMC.2022010107

APA

Verma, P., Dumka, A., Bhardwaj, A., & Ashok, A. (2022). Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA). International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-21. http://doi.org/10.4018/IJAMC.2022010107

Chicago

Verma, Parag, et al. "Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-21. http://doi.org/10.4018/IJAMC.2022010107

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Abstract

This research presents a way of feature selection problem for classification of sentiments that use ensemble-based classifier. This includes a hybrid approach of minimum redundancy and maximum relevance (mRMR) technique and Forest Optimization Algorithm (FOA) (i.e. mRMR-FOA) based feature selection. Before applying the FOA on sentiment analysis, it has been used as feature selection technique applied on 10 different classification datasets publically available on UCI machine learning repository. The classifiers for example k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Naïve Bayes used the ensemble based algorithm for available datasets. The mRMR-FOA uses the Blitzer’s dataset (customer reviews on electronic products survey) to select the significant features. The classification of sentiments has noticed to improve by 12 to 18%. The evaluated results are further enhanced by the ensemble of k-NN, NB and SVM with an accuracy of 88.47% for the classification of sentiment analysis task.