AQSA: Aspect-Based Quality Sentiment Analysis for Multi-Labeling with Improved ResNet Hybrid Algorithm
-
Published:2023-03-08
Issue:6
Volume:12
Page:1298
-
ISSN:2079-9292
-
Container-title:Electronics
-
language:en
-
Short-container-title:Electronics
Author:
Irfan Muhammad1ORCID, Ayub Nasir2ORCID, Ahmed Qazi Arbab3ORCID, Rahman Saifur1ORCID, Bashir Muhammad Salman4, Nowakowski Grzegorz5ORCID, Alqhtani Samar M.6ORCID, Sieja Marek5ORCID
Affiliation:
1. Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia 2. Department of Software Engineering, Faculty of Computing, Capital University of Science and Technology, Islamabad 44000, Pakistan 3. Department of Software Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan 4. Department of Computer Science, Virtual University of Pakistan, Lahore 54700, Pakistan 5. Facultyof Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Str., 31-155 Cracow, Poland 6. Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Abstract
Sentiment analysis (SA) is an area of study currently being investigated in text mining. SA is the computational handling of a text’s views, emotions, subjectivity, and subjective nature. The researchers realized that generating generic sentiment from textual material was inadequate, so they developed SA to extract expressions from textual information. The problem of removing emotional aspects through multi-labeling based on data from certain aspects may be resolved. This article proposes the swarm-based hybrid model residual networks with sand cat swarm optimization (ResNet-SCSO), a novel method for increasing the precision and variation of learning the text with the multi-labeling method. Contrary to existing multi-label training approaches, ResNet-SCSO highlights the diversity and accuracy of methodologies based on multi-labeling. Five distinct datasets were analyzed (movies, research articles, medical, birds, and proteins). To achieve accurate and improved data, we initially used preprocessing. Secondly, we used the GloVe and TF-IDF to extract features. Thirdly, a word association is created using the word2vec method. Additionally, the enhanced data are utilized for training and validating the ResNet model (tuned with SCSO). We tested the accuracy of ResNet-SCSO on research article, medical, birds, movie, and protein images using the aspect-based multi-labeling method. The accuracy was 95%, 96%, 97%, 92%, and 96%, respectively. With multi-label datasets of varying dimensions, our proposed model shows that ResNet-SCSO is significantly better than other commonly used techniques. Experimental findings confirm the implemented strategy’s success compared to existing benchmark methods.
Funder
Faculty of Electrical and Computer Engineering, Cracow University of Technology and the Ministry of Science and Higher Education, Republic of Poland
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference49 articles.
1. Sparse attention based separable dilated convolutional neural network for targeted sentiment analysis;Gan;Knowl.-Based Syst.,2020 2. TDAM: A topic-dependent attention model for sentiment analysis;Pergola;Inf. Process. Manag.,2019 3. Hu, M., Peng, Y., Huang, Z., Li, D., and Lv, Y. (2019). Open-domain targeted sentiment analysis via span-based extraction and classification. arXiv. 4. Luo, Y., Cai, H., Yang, L., Qin, Y., Xia, R., and Zhang, Y. (2022). Challenges for Open-domain Targeted Sentiment Analysis. arXiv. 5. Truong, Q.T., and Lauw, H.W. (2019, January 27). Vistanet: Visual aspect attention network for multimodal sentiment analysis. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|