Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

被引:3
作者
Eshmawi, Ala' A. [1 ]
Alhumyani, Hesham [2 ]
Khalek, Sayed Abdel [3 ]
Saeed, Rashid A. [2 ]
Ragab, Mahmoud [4 ]
Mansour, Romany F. [5 ]
机构
[1] Univ Jeddah, Coll Comp Sci & Engn, Dept Cybersecur, Jeddah, Saudi Arabia
[2] Taif Univ, Coll Comp & Informat Technol, Dept Comp Engn, At Taif 21944, Saudi Arabia
[3] Taif Univ, Fac Sci, Dept Math, At Taif 21944, Saudi Arabia
[4] King Abdulaziz Univ, Ctr Artificial Intelligence Precis Med, Jeddah 21589, Saudi Arabia
[5] New Valley Univ, Fac Sci, Dept Math, El Kharga 72511, Egypt
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2022年 / 71卷 / 02期
关键词
Opinion mining; sentiment analysis; fuzzy neural network; metaheuristics; feature extraction; classification;
D O I
10.32604/cmc.2022.021833
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Sentiment analysis or Opinion Mining (OM) has gained signifi-cant 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 Opti-mization 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.
引用
收藏
页码:2543 / 2557
页数:15
相关论文
共 20 条
[1]   A Hybrid Semantic Knowledgebase-Machine Learning Approach for Opinion Mining [J].
Alfrjani, Rowida ;
Osman, Taha ;
Cosma, Georgina .
DATA & KNOWLEDGE ENGINEERING, 2019, 121 :88-108
[2]   Opinion Mining and Sentiment Polarity on Twitter and Correlation Between Events and Sentiment [J].
Barnaghi, Peiman ;
Breslin, John G. ;
Ghaffari, Parsa .
PROCEEDINGS 2016 IEEE SECOND INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING SERVICE AND APPLICATIONS (BIGDATASERVICE 2016), 2016, :52-57
[3]   Opinion mining and emotion recognition applied to learning environments [J].
Barron Estrada, Maria Lucia ;
Zatarain Cabada, Ramon ;
Oramas Bustillos, Raul ;
Graff, Mario .
EXPERT SYSTEMS WITH APPLICATIONS, 2020, 150
[4]  
Brammya G., 2019, COMPUT J, DOI [10.1093/comjnl/bxy133, 10.1093/COMJNL/BXY133, DOI 10.1093/COMJNL/BXY133]
[5]   Analysis of Machine Learning Algorithms for Opinion Mining in Different Domains [J].
Gamal, Donia ;
Alfonse, Marco ;
El-Horbaty, El-Sayed M. ;
Salem, Abdel-Badeeh M. .
MACHINE LEARNING AND KNOWLEDGE EXTRACTION, 2019, 1 (01) :224-234
[6]  
Jeong Y, 2018, PORTL INT CONF MANAG
[7]   OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks [J].
Keyvanpour, Mohammadreza ;
Zandian, Zahra Karimi ;
Heidarypanah, Maryam .
SOCIAL NETWORK ANALYSIS AND MINING, 2020, 10 (01)
[8]  
Kim W.Y., 2009, Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, P270
[9]   Fuzzy Neural Network for Pattern Classification [J].
Kulkarni, Arun ;
Kulkarni, Nikita .
INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE, 2020, 167 :2606-2616
[10]   Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering [J].
Lakshmanaprabu, S. K. ;
Shankar, K. ;
Gupta, Deepak ;
Khanna, Ashish ;
Rodrigues, Joel J. P. C. ;
Pinheiro, Placido R. ;
de Albuquerque, Victor Hugo C. .
COMPLEXITY, 2018,