Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data

Vegi Harendranath and Sireesha Rodda
Additional contact information
Vegi Harendranath: Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM University (Deemed), India.
Sireesha Rodda: Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM University (Deemed), India

International Journal of Web Services Research (IJWSR), 2022, vol. 19, issue 1, 1-25

Abstract: This paper proposes an effective and optimal sentiment classification method named Penguin Rider optimization algorithm-based Deep Recurrent Neural Network (PeROA-based Deep RNN) to perform sentiment classification using political reviews. However, the proposed PeROA is developed by incorporating the Penguins Search Optimization Algorithm (PeSOA) with the Rider Optimization Algorithm (ROA). The sentiment classification process is progressed using the Deep RNN classifier, which in turn generate the optimal solution based on the fitness measure. Accordingly, the function with the minimal error value is accepted as the best solution. The sentiment-based features enable the classifier to perform better classification result with respect to the sentiment tweets. However, the proposed PeROA-based Deep RNN obtained better performance using the metrics, like accuracy, sensitivity, specificity, recall, F-measure, thread score, NPV, FPR,FNR and FDR with the values of 92.030%, 92.030%, 92.235%, 92.030%, 92.030%, 92.030%, 92.030%, 3.105%, 3.11%, and 3.105%, respectively.

Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWSR.299019 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:19:y:2022:i:1:p:1-25

Access Statistics for this article

International Journal of Web Services Research (IJWSR) is currently edited by Liang-Jie Zhang

More articles in International Journal of Web Services Research (IJWSR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().