AQSA: Aspect-based quality sentiment analysis for multi-labeling with improved ResNet hybrid algorithm
Authors:
- Muhammad Irfan,
- Nasir Ayub,
- Qazi Arbab Ahmed,
- Saifur Rahman,
- Muhammad Salman Bashir,
- Grzegorz Nowakowski,
- Samar M. Alqhtani,
- Marek Sieja
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.
- Record ID
- CUTed85e7a9ca664dee97eb35dde90ff47f
- Publication categories
- ;
- Author
- Journal series
- Electronics (Switzerland), ISSN , e-ISSN 2079-9292, Bimonthly
- Issue year
- 2023
- Vol
- 12
- No
- 6
- Pages
- [1-26]
- Article number
- 1298
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 25-26; Bibliografia (liczba pozycji) - 49; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 12, Iss. 6
- Substantive notes
- This article belongs to the Special Issue: Artificial Intelligence Technologies and Applications
- Keywords in English
- multi-labeling, sentiment analysis, deep learning, optimization techniques, processing textual data
- ASJC Classification
- ; ; ; ;
- DOI
- DOI:10.3390/electronics12061298 Opening in a new tab
- URL
- https://www.mdpi.com/2079-9292/12/6/1298/htm Opening in a new tab
- Related project
- [E-1/2023] Faculty of Electrical and Computer Engineering, Cracow University of Technology and the Ministry of Science and Higher Education. . Project leader at PK: , ,
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 1
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTed85e7a9ca664dee97eb35dde90ff47f/
- URN
urn:pkr-prod:CUTed85e7a9ca664dee97eb35dde90ff47f
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.