Work

Unifying Aesthetic Evaluation via Multimodal Annotation and Fine-Grained Sentiment Analysis

Year: 2026

Type: article

Abstract: With the rapid growth of visual content, automated aesthetic evaluation has become increasingly important. However, existing research faces three key challenges: (1) the absence of datasets combining Image Aesthetic Assessment (IAA) scores and Image Aesthetic Captioning (IAC) descriptions; (2) limited integration of quantitative scores and qualitative text, hindering comprehensive modeling; (3) the subjective nature of aesthetics, which complicates consistent fine-grained evaluation. To tackle these issues, we propose a unified multimodal framework. To address the lack of data, we develop the Textual Aesthetic Sentiment Labeling Pipeline (TASLP) for automatic annotation and construct the Reddit Multimodal Sentiment Dataset (RMSD) with paired IAA and IAC labels. To improve annotation integration, we introduce the Aesthetic Category Sentiment Analysis (ACSA) task, which models fine-grained aesthetic attributes across modalities. To handle subjectivity, we design two models—LAGA for IAA and ACSFM for IAC—that leverage ACSA features to enhance consistency and interpretability. Experiments on RMSD and public benchmarks show that our approach alleviates data limitations and delivers competitive performance, highlighting the effectiveness of fine-grained sentiment modeling and multimodal learning in aesthetic evaluation.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc10010037

Authors: Kai Liu, Hangyu Xiong, Jinyi Zhang, Min Peng

Institutions: Wuhan University, Technical University of Denmark, University of California, Los Angeles

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funder: National Natural Science Foundation of China