Open Access

ARTICLE

Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

by Ala’ A. Eshmawi¹, Hesham Alhumyani², Sayed Abdel Khalek³, Rashid A. Saeed², Mahmoud Ragab⁴, Romany F. Mansour^5,*

1 Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
2 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Department of Mathematics, Faculty of Sciences, Taif University, Taif, 21944, Saudi Arabia
4 Centre for Artificial Intelligence in Precision Medicine, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
5 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt

* Corresponding Author: Romany F. Mansour. Email:

Computers, Materials & Continua 2022, 71(2), 2543-2557. https://doi.org/10.32604/cmc.2022.021833

Received 16 July 2021; Accepted 27 August 2021; Issue published 07 December 2021

Abstract

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recent years. Likewise, Machine Learning (ML) approaches is one of the interesting research domains that are highly helpful and are increasingly applied in several business domains. In this background, the current research paper focuses on the design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviated as DHOA-FNN model. The proposed DHOA-FNN technique involves four different stages namely, preprocessing, feature extraction, classification, and parameter tuning. In addition to the above, the proposed DHOA-FNN model has two stages of feature extraction namely, Glove and N-gram approach. Moreover, FNN model is utilized as a classification model whereas GTOA is used for the optimization of parameters. The novelty of current work is that the GTOA is designed to tune the parameters of FNN model. An extensive range of simulations was carried out on the benchmark dataset and the results were examined under diverse measures. The experimental results highlighted the promising performance of DHOA-FNN model over recent state-of-the-art techniques with a maximum accuracy of 0.9928.

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Keywords

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