Muhammad Lawan

Adamu Lawan, Juhua Pu, Haruna Yunusa et al.

Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. However, attention mechanisms and neural network models struggle with syntactic constraints. The quadratic complexity of attention mechanisms also limits their adoption for capturing long-range dependencies between aspect and opinion words in ABSA. This complexity can lead to the misinterpretation of irrelevant contextual words, restricting their effectiveness to short-range dependencies. To address the above problem, we present a novel approach to enhance long-range dependencies between aspect and opinion words in ABSA (MambaForGCN). This approach incorporates syntax-based Graph Convolutional Network (SynGCN) and MambaFormer (Mamba-Transformer) modules to encode input with dependency relations and semantic information. The Multihead Attention (MHA) and Selective State Space model (Mamba) blocks in the MambaFormer module serve as channels to enhance the model with short and long-range dependencies between aspect and opinion words. We also introduce the Kolmogorov-Arnold Networks (KANs) gated fusion, an adaptive feature representation system that integrates SynGCN and MambaFormer and captures non-linear, complex dependencies. Experimental results on three benchmark datasets demonstrate MambaForGCN's effectiveness, outperforming state-of-the-art (SOTA) baseline models.

Adamu Lawan, Juhua Pu, Haruna Yunusa et al.

Multimodal Aspect-based Sentiment Analysis (MABSA) enhances sentiment detection by integrating textual data with complementary modalities, such as images, to provide a more refined and comprehensive understanding of sentiment. However, conventional attention mechanisms, despite notable benchmarks, are hindered by quadratic complexity, limiting their ability to fully capture global contextual dependencies and rich semantic information in both modalities. To address this limitation, we introduce DualKanbaFormer, a novel framework that leverages parallel Textual and Visual KanbaFormer modules for robust multimodal analysis. Our approach incorporates Aspect-Driven Sparse Attention (ADSA) to dynamically balance coarse-grained aggregation and fine-grained selection for aspect-focused precision, ensuring the preservation of both global context awareness and local precision in textual and visual representations. Additionally, we utilize the Selective State Space Model (Mamba) to capture extensive global semantic information across both modalities. Furthermore, We replace traditional feed-forward networks and normalization with Kolmogorov-Arnold Networks (KANs) and Dynamic Tanh (DyT) to enhance non-linear expressivity and inference stability. To facilitate the effective integration of textual and visual features, we design a multimodal gated fusion layer that dynamically optimizes inter-modality interactions, significantly enhancing the models efficacy in MABSA tasks. Comprehensive experiments on two publicly available datasets reveal that DualKanbaFormer consistently outperforms several state-of-the-art (SOTA) models.

Adamu Lawan, Juhua Pu, Haruna Yunusa et al.

Aspect-based Sentiment Analysis (ABSA) is a crucial NLP task that extracts fine-grained opinions and sentiments from text, such as product reviews and customer feedback. Existing methods often trade off efficiency for performance: traditional LSTM or RNN models struggle to capture long-range dependencies, transformer-based methods are computationally costly, and Mamba-based approaches rely on CUDA and weaken local dependency modeling. The recently proposed Extended Long Short-Term Memory (xLSTM) model offers a promising alternative by effectively capturing long-range dependencies through exponential gating and enhanced memory variants, sLSTM for modeling local dependencies, and mLSTM for scalable, parallelizable memory. However, xLSTM's application in ABSA remains unexplored. To address this, we introduce Aspect-aware Flip-and-Fuse xLSTM (AF-MAT), a framework that leverages xLSTM's strengths. AF-MAT features an Aspect-aware matrix LSTM (AA-mLSTM) mechanism that introduces a dedicated aspect gate, enabling the model to selectively emphasize tokens semantically relevant to the target aspect during memory updates. To model multi-scale context, we incorporate a FlipMix block that sequentially applies a partially flipped Conv1D (pf-Conv1D) to capture short-range dependencies in reverse order, followed by a fully flipped mLSTM (ff-mLSTM) to model long-range dependencies via full sequence reversal. Additionally, we propose MC2F, a lightweight Multihead Cross-Feature Fusion based on mLSTM gating, which dynamically fuses AA-mLSTM outputs (queries and keys) with FlipMix outputs (values) for adaptive representation integration. Experiments on three benchmark datasets demonstrate that AF-MAT outperforms state-of-the-art baselines, achieving higher accuracy in ABSA tasks.