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Ensemble incremental deep multiple layer perceptron model – sentiment analysis application

Ensemble incremental deep multiple layer perceptron model – sentiment analysis application The purpose of this paper is to enhance the accuracy of classification of streaming big data sets with lesser processing time. This kind of social analytics would contribute to society with inferred decisions at a correct time. The work is intended for streaming nature of Twitter data sets.Design/methodology/approachIt is a demanding task to analyse the increasing Twitter data by the conventional methods. The MapReduce (MR) is used for quickest analytics. The online feature selection (OFS) accelerated bat algorithm (ABA) and ensemble incremental deep multiple layer perceptron (EIDMLP) classifier is proposed for Feature Selection and classification. Three Twitter data sets under varied categories are investigated (product, service and emotions). The proposed model is compared with Particle Swarm Optimization, Accelerated Particle Swarm Optimization, accelerated simulated annealing and mutation operator (ASAMO). Feature Selection algorithms and classifiers such as Naïve Bayes, support vector machine, Hoeffding tree and fuzzy minimal consistent class subset coverage with the k-nearest neighbour (FMCCSC-KNN).FindingsThe proposed model is compared with PSO, APSO, ASAMO. Feature Selection algorithms, and classifiers such as Naïve Bayes (NB), support vector machine (SVM), Hoeffding Tree (HT), and Fuzzy Minimal Consistent Class Subset Coverage with the K-Nearest Neighbour (FMCCSC-KNN). The outcome of the work has achieved an accuracy of 99%, 99.48%, 98.9% for the given data sets with the processing time of 0.0034, 0.0024, 0.0053, seconds respectively.Originality/valueA novel framework is proposed for Feature Selection and classification. The work is compared with the authors’ previously developed classifiers with other state-of-the-art Feature Selection and classification algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Information Systems Emerald Publishing

Ensemble incremental deep multiple layer perceptron model – sentiment analysis application

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References (20)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1744-0084
DOI
10.1108/ijwis-05-2021-0056
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to enhance the accuracy of classification of streaming big data sets with lesser processing time. This kind of social analytics would contribute to society with inferred decisions at a correct time. The work is intended for streaming nature of Twitter data sets.Design/methodology/approachIt is a demanding task to analyse the increasing Twitter data by the conventional methods. The MapReduce (MR) is used for quickest analytics. The online feature selection (OFS) accelerated bat algorithm (ABA) and ensemble incremental deep multiple layer perceptron (EIDMLP) classifier is proposed for Feature Selection and classification. Three Twitter data sets under varied categories are investigated (product, service and emotions). The proposed model is compared with Particle Swarm Optimization, Accelerated Particle Swarm Optimization, accelerated simulated annealing and mutation operator (ASAMO). Feature Selection algorithms and classifiers such as Naïve Bayes, support vector machine, Hoeffding tree and fuzzy minimal consistent class subset coverage with the k-nearest neighbour (FMCCSC-KNN).FindingsThe proposed model is compared with PSO, APSO, ASAMO. Feature Selection algorithms, and classifiers such as Naïve Bayes (NB), support vector machine (SVM), Hoeffding Tree (HT), and Fuzzy Minimal Consistent Class Subset Coverage with the K-Nearest Neighbour (FMCCSC-KNN). The outcome of the work has achieved an accuracy of 99%, 99.48%, 98.9% for the given data sets with the processing time of 0.0034, 0.0024, 0.0053, seconds respectively.Originality/valueA novel framework is proposed for Feature Selection and classification. The work is compared with the authors’ previously developed classifiers with other state-of-the-art Feature Selection and classification algorithms.

Journal

International Journal of Web Information SystemsEmerald Publishing

Published: Dec 1, 2021

Keywords: Feature selection; Sentiment analysis; MapReduce; EIDMLP; OFS-ABA

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