The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)
|
|
Author(s): Parag Verma (Uttaranchal University, Dehradun, India), Ankur Dumka (Computer Science and Engineering, Women Institute of Technology, Dehradun, India), Anuj Bhardwaj (Computer Science and Engineering, Chandigarh University, Punjab, India)and Alaknanda Ashok (College of Technology, G. B. Pant University of Agriculture and Technology, India)
Copyright: 2022
Volume: 13
Issue: 1
Pages: 21
Source title:
International Journal of Applied Metaheuristic Computing (IJAMC)
Editor(s)-in-Chief: Peng-Yeng Yin (Ming Chuan University, Taiwan)
DOI: 10.4018/IJAMC.2022010107
Purchase
|
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.
Related Content
|
Abid Sabrina, Debbat Fatima.
© 2024.
20 pages.
|
|
Maryam AlJame, Aisha Alnoori, Mohammad G. Alfailakawi, Imtiaz Ahmad.
© 2023.
27 pages.
|
|
Trust Tawanda, Philimon Nyamugure, Elias Munapo, Santosh Kumar.
© 2023.
16 pages.
|
|
Sarab Almuhaideb, Najwa Altwaijry, Shahad AlMansour, Ashwaq AlMklafi, AlBandery Khalid AlMojel, Bushra AlQahtani, Moshail AlHarran.
© 2022.
22 pages.
|
|
Preeti Pragyan Mohanty, Subrat Kumar Nayak.
© 2022.
32 pages.
|
|
Sajad Ahmad Rather, P. Shanthi Bala.
© 2022.
39 pages.
|
|
Ines Sbai, Saoussen Krichen.
© 2022.
34 pages.
|
|
|