2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 16 , Issue 2 , PP: 60-67, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Nirmala Veluswamy 1 * , Jayanthi Boopathy 2
Doi: https://doi.org/10.54216/JISIoT.160205
The representational and learning power of tree-based deep-learning (DL) classification models makes them a popular choice for dimensional sentiment analysis (DSA). One variant, Tree-structured Convolutional neural network with long short-term Memory (TCL) stands out among many others for its ability to handle uncertainties and unexpected changes in input data while still producing promising Valence-Arousal (VA) predictions for text or image classes. However, the high memory complexity of this model becomes a challenge when dealing with large image/text datasets. To address this issue, this manuscript introduces a Lightweight Adversarial Attention TCL (LAATCL) model for DSA. The proposed model includes a clustering layer in conjunction with the ATCL to decrease memory complexity and enhance performance through reliable sample selection. This model comprises multi-convolution with a clustering layer that utilizes Group-Sparse Non-negative Matrix Factorization (GSNMF) for clustering highly correlated samples. By learning informative and discriminative latent variables across labels, GSNMF helps identify and select samples closest to the cluster centroid for input to the LSTM network, resulting in reduced memory complexity and improved accuracy. The LATCL model outperformed traditional models in experiments conducted on the SST and CIFAR-10 datasets, with accuracies of 93.57% and 95.25%, respectively, demonstrating its usefulness.
Tree-based deep learning , Group-Sparse Non-negative Matrix Factorization , Clustering , Dimensional sentiment analysis
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