Home > Published Issues > 2023 > Volume 14, No. 2, 2023 >
JAIT 2023 Vol.14(2): 342-349
doi: 10.12720/jait.14.2.342-349

Firefly with Levy Based Feature Selection with Multilayer Perceptron for Sentiment Analysis

D. Elangovan 1,* and V. Subedha 2
1. School of Computing, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India
2. Department of Computer Science and Engineering, Panimalar Institute of Technology, Chennai, India; Email: vsubedhav@gmail.com (V.S.)
*Correspondence: elangovanduraimu@gmail.com (D.E.)

Manuscript received January 1, 2022; revised March 25, 2022; accepted September 13, 2022; published April 17, 2023.

Abstract—Sentimental Analysis (SA) has recently received a lot of attention in decision-making because it can extract and analyze sentiments from web-based reviews made by customers. In this case, SA has been used as a Sentiment Classification (SC) problem, in which reviews are typically labeled as positive or negative depending upon online reviews. By combining FS (Feature Selection) and categorization, this work proposes an effective SA method for internet reviews. FireFly (FF) and Levy Flights (FFL) algorithms have been used for extracting features of web-based reviews, and also the Multilayer Perceptron (MLP) framework has been used to categorize the emotions. A standard DVD database displayed the efficacy of the FF-MLP model on the testing. The outcome shows that the suggested FF-MLP system accomplishes enhanced performance with maximum sensitivity of 98.97%, specificity of 93.67%, accuracy of 97.97%, F-score of 98.75, and kappa of 93.32%.
Keywords—sentiment classification, data mining, firefly algorithm, feature selection, multilayer perceptron

Cite: D. Elangovan and V. Subedha, "Firefly with Levy Based Feature Selection with Multilayer Perceptron for Sentiment Analysis," Journal of Advances in Information Technology, Vol. 14, No. 2, pp. 342-349, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.