Title: Deep hybridisation of cross-space adaptive filter with matrix completion of dual adaptive jumping graph neural networks for sentiment classification systems

Authors: K.R. Srinath; B. Indira

Addresses: Department of Informatics, Osmania University, Hyderabad, Telangana, India ' Department of MCA, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India

Abstract: In the era of big data and complex social media interactions, sentiment analysis and recommender systems (RS) face significant challenges due to information overload and the dynamic nature of user preferences. To address these issues, a combination of cross-space adaptive filter (CSF) with matrix completion and dual adaptive jumping graph neural networks (DualAJGNN) is introduced. The proposed model enhances sentiment classification and provides accurate recommendations by considering users' sentimental attributes and social influence while overcoming limitations of existing methods such as over-smoothing in graph convolutional networks (GCNs). This research optimises neighbourhood aggregation in GCNs using CSF. Additionally, a graph neural network (GNN) model combines multiple preferences derived from extended user behaviours to create clusters, forming an interest graph for sequential recommendation. The effectiveness of the proposed model is validated using three datasets, achieving an accuracy of about 97%, which demonstrates its superiority compared to existing techniques for recommendation systems and sentiment classification.

Keywords: sentiment analysis; recommender systems; graph convolutional networks; GCNs; cross-space adaptive filter; CSF; dual adaptive jumping graph neural networks; GNNs; information overload.

DOI: 10.1504/IJIEI.2026.154019

International Journal of Intelligent Engineering Informatics, 2026 Vol.14 No.2, pp.231 - 258

Received: 06 Sep 2024
Accepted: 13 Nov 2024
Published online: 10 Jun 2026 *

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