AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis (2306.01004v1)

Abstract: Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspects from text-image pairs and recognize their sentiments. Existing methods make great efforts to align the whole image to corresponding aspects. However, different regions of the image may relate to different aspects in the same sentence, and coarsely establishing image-aspect alignment will introduce noise to aspect-based sentiment analysis (i.e., visual noise). Besides, the sentiment of a specific aspect can also be interfered by descriptions of other aspects (i.e., textual noise). Considering the aforementioned noises, this paper proposes an Aspect-oriented Method (AoM) to detect aspect-relevant semantic and sentiment information. Specifically, an aspect-aware attention module is designed to simultaneously select textual tokens and image blocks that are semantically related to the aspects. To accurately aggregate sentiment information, we explicitly introduce sentiment embedding into AoM, and use a graph convolutional network to model the vision-text and text-text interaction. Extensive experiments demonstrate the superiority of AoM to existing methods. The source code is publicly released at https://github.com/SilyRab/AoM.

Overview of AoM: Detecting Aspect-Oriented Information for Multimodal Aspect-Based Sentiment Analysis

This paper introduces the Aspect-oriented Method (AoM) as a novel approach for tackling Multimodal Aspect-Based Sentiment Analysis (MABSA). MABSA involves extracting aspects from text-image pairs and determining their associated sentiment polarities. Existing methods often struggle with properly aligning images to textual aspects, leading to erroneous sentiment analysis due to the introduction of visual and textual noise. Visual noise arises from irrelevant or unrelated image regions, while textual noise results from unnecessary or misleading textual descriptions.

AoM addresses these challenges by introducing an Aspect-Aware Attention Module (A$^3$M) and an Aspect-Guided Graph Convolutional Network (AG-GCN). The A$^3$M is designed to filter and align relevant visual and textual information, whereas the AG-GCN is focused on aggregating sentiment information by leveraging a graph-based structure to model vision-text and text-text interactions. Successfully, AoM significantly reduces noise, enhancing the accuracy of sentiment analysis.

Key Components and Methodology

1. Aspect-Aware Attention Module (A$^3$M): This module performs fine-grained alignment by selecting relevant image blocks and textual tokens associated with the identified aspects. By using an attention mechanism driven by extracted candidate aspects, A$^3$M computes aspect-related hidden representations. This nuanced alignment effectively mitigates visual noise from irrelevant image regions and ensures aspect-centric analysis.
2. Aspect-Guided Graph Convolutional Network (AG-GCN): Integrating sentiment embeddings, this module constructs a multimodal association matrix. It employs graph convolutional networks to model complex interactions within the image-text pair. The network considers aspect-to-image-block alignments and textual dependencies, thereby providing a coherent representation of sentiment information. Notably, external affective knowledge from SenticNet enhances the sentiment processing capability of AG-GCN.
3. Pre-training and Performance: A$^3$M undergoes pre-training using the TRC dataset to refine image-text relations, which aids in learning better parameter alignment. The proposed model is evaluated on Twitter2015 and Twitter2017 datasets, demonstrating superior performance over state-of-the-art methods in terms of Precision, Recall, and F1 score across tasks related to MABSA, Multimodal Aspect Term Extraction (MATE), and Multimodal Aspect-oriented Sentiment Classification (MASC).

Empirical Results and Implications

The results reveal AoM's strong performance, notably achieving a 2% and 1.2% improvement on F1 scores for MABSA in Twitter2015 and Twitter2017 datasets, respectively, over the next best models. Moreover, AoM's exemplar performance in MASC indicates its robustness in effectively utilizing aspect-relevant multimodal information to discern sentiments accurately.

The introduction of AoM offers both practical and theoretical contributions. Practically, it's a significant step toward improved sentiment analysis by addressing the intricacies of multimodal data. Theoretically, it proposes a sophisticated integration of attention mechanisms and graph convolutional networks for multimodal sentiment tasks. As future work, fine-tuning of such models using larger, more diverse datasets could further enhance their applicability and precision in real-world scenarios.

In summary, AoM advances the field of MABSA by providing a structured means to handle the complexities of multimodal sentiment analysis, ensuring nuanced, aspect-driven analysis enhanced with both visual and textual components. This work underscores the potential of integrating attention mechanisms with graph-based approaches to tackle the inherent challenges in sentiment analysis, potentially opening avenues for more refined models in the domain of AI-driven sentiment prediction.