# DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1410.8586 · HTML: https://mlsanity.com/paper/1410.8586/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2014.

## Built on by (most cited first, 24 of 24)

- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (2021)
- [GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition](https://mlsanity.com/paper/2312.04293/) (2023)
- [MDAN: Multi-level Dependent Attention Network for Visual Emotion Analysis](https://mlsanity.com/paper/2203.13443/) (2022)
- [Stimuli-Aware Visual Emotion Analysis](https://mlsanity.com/paper/2109.01812/) (2021)
- [Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2204.07955/) (2022)
- [SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network](https://mlsanity.com/paper/2110.12334/) (2021)
- [Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality](https://mlsanity.com/paper/2103.06541/) (2021)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [Learning to Compose Diversified Prompts for Image Emotion Classification](https://mlsanity.com/paper/2201.10963/) (2022)
- [AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.01004/) (2023)
- [Seeking Subjectivity in Visual Emotion Distribution Learning](https://mlsanity.com/paper/2207.11875/) (2022)
- [On the use of Vision-Language models for Visual Sentiment Analysis: a study on CLIP](https://mlsanity.com/paper/2310.12062/) (2023)
- [Automatic Modeling of Social Concepts Evoked by Art Images as Multimodal Frames](https://mlsanity.com/paper/2110.07420/) (2021)
- [Training A Small Emotional Vision Language Model for Visual Art Comprehension](https://mlsanity.com/paper/2403.11150/) (2024)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [The Emotions of the Crowd: Learning Image Sentiment from Tweets via Cross-modal Distillation](https://mlsanity.com/paper/2304.14942/) (2023)
- [Enhanced Multimodal Aspect-Based Sentiment Analysis by LLM-Generated Rationales](https://mlsanity.com/paper/2505.14499/) (2025)
- [Multimodal LLMs See Sentiment](https://mlsanity.com/paper/2508.16873/) (2025)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis](https://mlsanity.com/paper/2109.07509/) (2021)
- [A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2412.08489/) (2024)
- [Attributes-aware Visual Emotion Representation Learning](https://mlsanity.com/paper/2504.06578/) (2025)
- [Analyzing Image Beyond Visual Aspect: Image Emotion Classification via Multiple-Affective Captioning](https://mlsanity.com/paper/2511.23115/) (2025)
- [EmoLat: Text-driven Image Sentiment Transfer via Emotion Latent Space](https://mlsanity.com/paper/2601.12079/) (2026)

## Abstract (arXiv)

This paper introduces a visual sentiment concept classification method based on deep convolutional neural networks (CNNs). The visual sentiment concepts are adjective noun pairs (ANPs) automatically discovered from the tags of web photos, and can be utilized as effective statistical cues for detecting emotions depicted in the images. Nearly one million Flickr images tagged with these ANPs are downloaded to train the classifiers of the concepts. We adopt the popular model of deep convolutional neural networks which recently shows great performance improvement on classifying large-scale web-based image dataset such as ImageNet. Our deep CNNs model is trained based on Caffe, a newly developed deep learning framework. To deal with the biased training data which only contains images with strong sentiment and to prevent overfitting, we initialize the model with the model weights trained from ImageNet. Performance evaluation shows the newly trained deep CNNs model SentiBank 2.0 (or called DeepSentiBank) is significantly improved in both annotation accuracy and retrieval performance, compared to its predecessors which mainly use binary SVM classification models.