# From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction

arXiv: https://arxiv.org/abs/1604.03489 · HTML: https://mlsanity.com/paper/1604.03489/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2016.

## Built on by (most cited first, 12 of 12)

- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (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)
- [On the use of Vision-Language models for Visual Sentiment Analysis: a study on CLIP](https://mlsanity.com/paper/2310.12062/) (2023)
- [Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection](https://mlsanity.com/paper/2204.11515/) (2022)
- [A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications](https://mlsanity.com/paper/2311.11250/) (2023)
- [Collaborative Image Understanding](https://mlsanity.com/paper/2210.11907/) (2022)
- [Follow Us and Become Famous! Insights and Guidelines From Instagram Engagement Mechanisms](https://mlsanity.com/paper/2301.06815/) (2023)
- [The Emotions of the Crowd: Learning Image Sentiment from Tweets via Cross-modal Distillation](https://mlsanity.com/paper/2304.14942/) (2023)
- [AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art](https://mlsanity.com/paper/2402.02453/) (2024)
- [Seeing The Words: Evaluating AI-generated Biblical Art](https://mlsanity.com/paper/2504.16974/) (2025)
- [Multimodal LLMs See Sentiment](https://mlsanity.com/paper/2508.16873/) (2025)
- [Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices](https://mlsanity.com/paper/2201.09933/) (2022)

## Abstract (arXiv)

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model.