In contrast to earlier ABSA studies primarily concentrating on individual sentiment components, recent research has ventured into more complex ABSA tasks encompassing multiple elements, including pair, triplet, and quadruple sentiment analysis. Quadruple sentiment analysis, also called aspect-category-opinion-sentiment quadruple Extraction (ACOSQE), aims to dissect aspect terms, aspect categories, opinion terms, and sentiment polarities while considering implicit sentiment within sentences. Nonetheless, a comprehensive overview of ACOSQE and its corresponding solutions is currently lacking. This is the precise gap that our survey seeks to address. To be more precise, we systematically reclassify all subtasks of ABSA, reorganizing existing research from the perspective of the involved sentiment elements, with a primary focus on the latest advancements in the ACOSQE task. Regarding solutions, our survey offers a comprehensive summary of the state-of-the-art utilization of language models within the ACOSQE task. Additionally, we explore the application of ChatGPT in sentiment analysis. Finally, we review emerging trends and discuss the challenges, providing insights into potential future directions for ACOSQE within the broader context of ABSA.

Targeted multimodal sentiment classification (TMSC) aims to identify the sentiment polarity of a target mentioned in a multimodal post. The majority of current studies on this task focus on mapping the image and the text to a high-dimensional space in order to obtain and fuse implicit representations, ignoring the rich semantic information contained in the images and not taking into account the contribution of the visual modality in the multimodal fusion representation, which can potentially influence the results of TMSC tasks. This paper proposes a general model for Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images (ITMSC) as a way to tackle these issues and improve the accuracy of multimodal sentiment analysis. Specifically, the ITMSC model can automatically adjust the contribution of images in the fusion representation through the exploitation of semantic descriptions of images and text similarity relations. Further, we propose a target-based attention module to capture the target-text relevance, an image-based attention module to capture the image-text relevance, and a target-image matching module based on the former two modules to properly align the target with the image so that fine-grained semantic information can be extracted. Our experimental results demonstrate that our model achieves comparable performance with several state-of-the-art approaches on two multimodal sentiment datasets. Our findings indicate that incorporating semantic descriptions of images can enhance our understanding of multimodal content and lead to improved sentiment analysis performance.

Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes, such as text and image, to accurately assess sentiment. However, conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities. This limitation is attributed to their training on unimodal data, and necessitates the use of complex fusion mechanisms for sentiment analysis. In this study, we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method. Our approach harnesses the power of transfer learning by utilizing a vision-language pre-trained model to extract both visual and textual representations in a unified framework. We employ a Transformer architecture to integrate these representations, thereby enabling the capture of rich semantic information in image-text pairs. To further enhance the representation learning of these pairs, we introduce our proposed multimodal contrastive learning method, which leads to improved performance in sentiment analysis tasks. Our approach is evaluated through extensive experiments on two publicly accessible datasets, where we demonstrate its effectiveness. We achieve a significant improvement in sentiment analysis accuracy, indicating the superiority of our approach over existing techniques. These results highlight the potential of multimodal sentiment analysis and underscore the importance of considering the intrinsic semantic connections between modalities for accurate sentiment assessment.

Targeted multimodal sentiment classification is frequently impeded by the semantic sparsity of social media content, where text is brief and context is implicit. Traditional methods that rely on direct concatenation of textual and visual features often fail to resolve the ambiguity of specific targets due to a lack of alignment between modalities. In this paper, we propose the Complementary Description Network (CDNet) to bridge this informational gap. CDNet incorporates automatically generated image descriptions as an additional semantic bridge, in contrast to methods that handle text and images as distinct streams. The framework enhances the input representation by directly translating visual content into text, allowing for more accurate interactions between the opinion target and the visual narrative. We further introduce a complementary reconstruction module that functions as a regularizer, forcing the model to retain deep semantic cues during fusion. Empirical results on the Twitter-2015 and Twitter-2017 benchmarks confirm that CDNet outperforms existing baselines. The findings suggest that visual-to-text augmentation is an effective strategy for compensating for the limited context inherent in short texts.