Aspect-based Sentiment Analysis via Synthetic Image Generation (2025)

Abstract: Recent advancements in Aspect-Based Sentiment Analysis (ABSA) have shown promising results, yet the semantics derived solely from textual data remain limited. To overcome this challenge, we propose a novel approach by venturing into the unexplored territory of generating sentimental images. Our method introduce a synthetic image generation framework tailored to produce images that are highly congruent with both textual and sentimental information for aspect-based sentiment analysis. Specifically, we firstly develop a supervised image generation model to generate synthetic images with alignment to both text and sentiment information. Furthermore, we employ a visual refinement technique to substantially enhance the quality and pertinence of the generated images. After that, we propose a multi-modal model to integrate both the original text and the synthetic images for aspect-based sentiment analysis. Extensive evaluations on multiple benchmark datasets demonstrate that our model significantly outperforms state-of-the-art methods. These results highlight the effectiveness of our supervised image generation approach in enhancing ABSA.

## introduction aspect-based sentiment analysis (absa) represents a fine-grained approach to text sentiment analysis, which pinpoints sentiment information pertinent to specific aspects and offers businesses and organizations deeper market insights. @xcite absa is commonly divided into two independent subtasks: aspect term extraction (ate) and aspect sentiment classification (asc). ate aims to extract all aspect terms from a given text, while asc is concerned with determining the sentiment polarity of each aspect @xcite . previous studies have demonstrated significant advancements in absa by utilizing pre-trained encoder-decoder language models @xcite . these studies have adopted various strategies, treating the class index @xcite , natural language descriptions @xcite , or the desired sentiment element sequence @xcite @xcite as the target for the generation model. despite their effectiveness, most of these studies have been limited to raw input textual data, neglecting other additional data sources that could enrich and complement textual absa systems. therefore, an increasing number of methods have begun to incorporate image information to enhance the performance of the absa task @xcite @xcite . integrating image information can offer several advantages. for one, visual elements can provide a more intuitive and vivid understanding of the text content, especially when dealing with complex sentiment expressions. images can also capture subtle emotional cues that might be overlooked in text alone, thereby enriching the sentiment analysis process. however, for original texts, it is a difficult endeavor to collect and acquire corresponding image information. furthermore, numerous original texts lack directly corresponding images. thus, it is essential to generate images based on the original texts. nevertheless, directly generating images that correspond to the original text presents multiple challenges. firstly, the generated images must be relevant to the original text not only in a literal sense but also in terms of sentiment. secondly, it is desirable for the generated images to incorporate additional information that can aid in sentiment analysis. to tackle these challenges, we introduce synthetic image generation framework tailored to produce images that are highly congruent with both textual and sentimental information for aspectbased sentiment analysis, as shown in 1. specifically, our framework is divided into two stages: in the first stage, we develop a supervised image generation model to generate synthetic images with alignment to both text and sentiment information. moreover, we employ visual refinement techniques to enhance the synthetic images by highlighting regions relevant to sentiment information and masking irrelevant areas, thereby enabling the images to focus more closely on sentiment-related visual cues. subsequently, we propose a multi-modal model that combines the original text with the synthetic images for aspect-based sentiment analysis. extensive experimental evaluations demonstrate that our model significantly outperforms existing state-of-the-art methods on multiple benchmark datasets, opening a new avenue for multimodal sentiment analysis and enhancing large language models' capabilities in visual-text understanding. ## related works in this section, we introduce two related topics of this study: aspect-based sentiment analysis, and multi-modal sentiment analysis. ## synthetic image generation as illustrated in figure 2 , we introduce a synthetic image generation framework tailored to produce images that are highly congruent with both textual and sentiment information for aspect-based sentiment analysis. specifically, our framework is divided into two stages: in the first stage, we develop a supervised image generation model to generate synthetic images aligned with both text and sentiment. furthermore, we employ a visual refinement technique to substantially enhance the quality and pertinence of the generated images, ensuring they more accurately reflect the aspects and sentiments conveyed in the original text. supervised synthetic image generation visual refinement for synthetic image fergus sunbathes to promote healing his hip after playing too hard . happy soul original text synthetic image aspect-based sentiment analysis aspect fergus polarity positive synthetic image generation overview sentiment-aware training data construction fine-tuning for synthetic image generation fine-grained visual prompting attention prompt on image original image (basketball, neu) mllm object detection and cropping sentiment-aware training data generation sentiment-aware training data generation 训练图片生成模型用的例子，要和其他例子不一 样 figure 3: an example of sentiment-aware training data construction. ## aspect-based sentiment analysis with synthetic image after we obtain the generated synthetic images with visual refinement, we propose a multi-modal aspect-based sentiment analysis model to integrate both original text and the synthetic images. ## experiments in this section, we introduce the datasets used for evaluation and the baseline methods employed for comparison. we then report the experimental results conducted from different perspectives, and analyze the effectiveness of the proposed model with different factors.