Research and Application Analysis of Image-Based Sentiment Analysis Methods

Abstract

Emotion, as the core of human subjective experience, exerts a profound impact on mental health and social interactions. With the development of computer vision technology, image-based emotion analysis has become a critical research direction, finding wide application in driving safety, advertising marketing, healthcare, and other fields. However, the diversity and complexity of emotional expressions, along with environmental interference and challenges in multimodal fusion, still hinder the improvement of its accuracy and robustness. This paper provides a comprehensive review and analysis of imagebased emotion analysis methods and their practical applications. First, it briefly introduces the research background and application value of this field, highlighting its key roles in scenarios such as driver/passenger emotion monitoring in autonomous driving and consumer emotion recognition in advertising. Subsequently, four categories of core technical methods are elaborated in detail. This paper also conducts a systematic comparison of the four method categories in terms of their core technical frameworks, key innovations, performance (efficiency and robustness), applicable scenarios, and limitations. Finally, it summarizes three major current challenges: the diversity of emotional expressions, data annotation issues, and multimodal fusion difficulties. Corresponding future directions are proposed, including enhancing model robustness and generalization, optimizing data and efficient models, and advancing adaptive multimodal fusion technologies, aiming to provide comprehensive technical references for the development and application of this field.