Title: Document-level sentiment analysis using Jaya chimp optimisation algorithm-enabled deep residual network

Authors: Manoj L. Bangare; Sampath Arpakkam Karuppan; Debarati Ghosal; Ashwin Perti; Sanjay Nakharu Prasad Kumar

Addresses: Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, India ' Presidency University, Yelahanka, Bengaluru, India ' Vidyalankar Institute of Technology, Vidyalankar College Marg, Wadala(E), Mumbai-400 037, India ' Department of Computer Science and Engineering, ABES Engineering College, Crossings Republik, Ghaziabad, Uttar Pradesh 201009, India ' San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132, USA

Abstract: Document-level sentiment classification automates the process of categorising text reviews on a single topic as representing negative or positive sentiments. Users and customers are intended to share comments and reviews about their products on various social network sites. One of these processing steps is the classification of emotions associated with the reviews. Therefore, this research paper introduces a robust sentiment analysis method, named Jaya chimp optimisation algorithm-enabled deep residual network (JayaChOA-enabled DRN) for document-level sentiment classification. The input is pre-processed and tokenised, and then the key features are extracted. Moreover, the DRN classifier is used for the sentiment classification where the optimal weights are computed using the JayaChOA. Meanwhile, the introduced JayaChOA is implemented by the incorporation of Jaya optimiser and chimp optimisation algorithm (ChOA). The JayaChOA-based DRN obtained the highest precision of 0.914, F-measure of 0.919, and recall of 0.925 using K-fold.

Keywords: sentiment analysis; deep learning; chimp optimisation algorithm; Jaya optimiser; natural language processing; NLP.

DOI: 10.1504/IJIIDS.2026.152766

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.2, pp.153 - 172

Accepted: 01 Aug 2024
Published online: 10 Apr 2026 *

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