Sentimental Image Generation for Aspect-based Sentiment Analysis (2025)

Abstract: Recent research work on textual Aspect-Based Sentiment Analysis (ABSA) have achieved promising performance. However, a persistent challenge lies in the limited semantics derived from the raw data. To address this issue, researchers have explored enhancing textual ABSA with additional augmentations, they either craft audio, text and linguistic features based on the input, or rely on user-posted images. Yet these approaches have their limitations: the former three formations are heavily overlap with the original data, which undermines their ability to be supplementary while the user-posted images are extremely dependent on human annotation, which not only limits its application scope to just a handful of text-image datasets, but also propagates the errors derived from human mistakes to the entire downstream loop. In this study, we explore the way of generating the sentimental image that no one has ever ventured before. We propose a novel Sentimental Image Generation method that can precisely provide ancillary visual semantics to reinforce the textual extraction as shown in Figure 1. Extensive experiments build a new SOTA performance in ACOS, ASQP and en-Phone datasets, underscoring the effectiveness of our method and highlighting a promising direction for expanding our features.

## introduction aspect-based sentiment analysis (absa) is a topic of increasing interest in the research community, it is comprised of four subtasks: aspect term extraction, opinion term extraction, aspect category classification, and aspect-level sentiment classification. ## related work research on absa typically progresses from addressing individual sub-tasks to tackling their intricate combinations. initially, the focus is often on predicting a single sentiment element @xcite @xcite @xcite . many studies also explore the joint extractions, aiming to capture more complex sentiment information @xcite @xcite . recently, there has been a growing interest in tackling the absa problem using generative approaches @xcite . these approaches involve treating the class index @xcite or the desired sentiment element sequence @xcite as the target of the generation model, feeding a prompt to generate the sequence of aspect terms and opinion words @xcite @xcite . furthermore, multi-view prompting (mvp) @xcite aggregates sentiment elements generated in different orders, mimicking human-like problem-solving processes from different views. some works subsequently explore augmenting features from extra modalities to provide additional semantics. there are initial attempts on linguistic features, such as syntactic @xcite and dependence trees @xcite , are combined into downstream models with linearization or graph networks. some works further explore audio, leveraging the pitches and tones in the speech @xcite @xcite to dig the implicit sentiment information behind the samples. recently, the rise of llms have introduced text generation-based approaches: atoss @xcite propose a plugand-play module that splits input sentence into multiple aspect-oriented sub-sentences; unigen (choi input review finetuned llm the pizza was very good, and the chef even came to ask for comments, which was very nice! pair: pizza, good pair: chef, nice aspect: pizza aspect: chef quad: pizza, good, food, positive quad: chef, nice, service, positive aspect emphasis pair emphasis quad emphasis a baked pizza sits on a wooden table. nearby, a chef leans slightly forward, engaging with diners a steaming pizza sits on a wooden table, with good toppings. nearby, the smiling chef engages nicely with diners, seeking their thoughts. a good taste pizza sits on a wooden table, half-eaten, with a diner satisfied with the food. nearby, a chef is engaging nicely in conversation, embodying excellent service. pizza/food chef positive chef pizza negative chef positive pizza/food service b) scene rewriting a) emphasis prediction generated sentimental image scene description c) sentimental image assessment best selection et al., 2024) producing zero-shot dataset based on the knowledge from llms and scrap (kim et al., 2024) distills chain-of-thought reasoning text and performs a vote over them. in contrast to previous studies, our research stands out by first introducing generated visual content to the textual absa task. this novel approach significantly surpasses prior augmentation methods in enhancing the extraction of aspect-level quadruples, and more importantly, extends the applicability of visual augmentations to scenarios where only text data is accessible. ## sentimental image generation for aspect-based sentiment analysis in this study, we propose a novel sentimental image generation method which generally includes two part: sentimental image generation and sentimental image assessment. as shown in figure 2 , we start by crafting scene descriptions with sentimental paraphrasing and generating a candidate pool of images based on them. subsequently, we assess the images' text-image relevance with sentimental image assessment to identify the most fitting image as illustrated in figure 2 (c ). we further bridge the textual and visual modality with a unified visionlanguage model showcased in figure 4 . we will discuss these steps one by one. ## cases studies we launch case studies to make a more intuitive comparison between the extraction result with and without our generated image in we show that generated sentimental images can effectively capture the intended target in the first two examples. the extraction without generated images in the first two example misses "harry potter studios" and "university in # berkeley" respectively, while our generated sentimental images successfully cover them, aiding the vlm in identifying the correct elements. furthermore, we illustrate that generated sentimental images can better convey sentiment polarity in the last two examples. the extraction without generated images in the third example successfully captures the aspect target but lacks discernible po- from the cases shown in ## conclusion in this study, we address the long-overlooked limitations of existing data augmentation methods of textual absa and shift our focus toward generating sentimental images from scratch as a promising alternative. with proposed sentimental image generation and assessment, we generate effective images to assist textual absa, achieving sota performance in multiple benchmarks. our results also validate that, in addition to the conventional approaches of incorporating extra user-posted features, leaning on machinesgenerated features generated from scratch could also be considered as an efficiently way to provide us with supplementary semantic insights.