Survey on Visual Sentiment Analysis

Abstract

Visual Sentiment Analysis aims to understand how images affect people, in terms of evoked emotions. Although this field is rather new, a broad range of techniques have been developed for various data sources and problems, resulting in a large body of research. This paper reviews pertinent publications and tries to present an exhaustive overview of the field. After a description of the task and the related applications, the subject is tackled under different main headings. The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view: emotional models, dataset definition, feature design. A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways. To this aim, this paper considers a structured formalization of the problem which is usually used for the analysis of text, and discusses it's suitability in the context of Visual Sentiment Analysis. The paper also includes a description of new challenges, the evaluation from the viewpoint of progress toward more sophisticated systems and related practical applications, as well as a summary of the insights resulting from this study.

This paper is a postprint of a paper accepted by IET Image Processing and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at the IET Digital Library

References in corpus (10)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Sentiment of Emojis
• DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
• Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks
• Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
• Visual Sentiment Prediction with Deep Convolutional Neural Networks
• Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
• Understanding Image Virality
• Time Matters: Multi-scale Temporalization of Social Media Popularity