Title: TDSSO: Tasmanian devil squirrel search optimisation enabled deep learning for ambiguity removal in aspect-based sentiment classification

Authors: Neelima S. Ambekar; Anant V. Nimkar

Addresses: Department of Computer Engineering, Sardar Patel Institute of Technology, University of Mumbai, Mumbai, India ' Department of Computer Engineering, Sardar Patel Institute of Technology, University of Mumbai, Mumbai, India

Abstract: An aspect-based sentiment classification is a fine-grained approach to extracting relevant information from online customer reviews. The proposed work addresses the problem of eliminating ambiguity about relevant feature extraction using context keywords. A deep learning (DL) approach, hierarchical deep learning for text (HDLTex), is used for sentiment categorisation based on the multiple features mined from the review data. Additionally, we employ an optimisation algorithm known as the Tasmanian devil squirrel search optimisation (TDSSO) to estimate the weight parameters of HDLTex. The proposed method outperforms the state-of-the-art models in assessing the efficacy of sentiment classification based on k-fold values and training data. The experimental results demonstrate significant improvements in the true positive rate, true negative rate, and testing accuracy with values of 0.926, 0.909, and 0.947, respectively.

Keywords: aspect-based sentiment classification; ambiguity; optimisation; deep learning; context keywords.

DOI: 10.1504/IJCSE.2025.149764

International Journal of Computational Science and Engineering, 2025 Vol.28 No.6, pp.662 - 681

Received: 26 Oct 2023
Accepted: 23 May 2024
Published online: 12 Nov 2025 *

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