# Visual Sentiment Prediction with Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1411.5731 · HTML: https://mlsanity.com/paper/1411.5731/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2014.

## Built on by (most cited first, 6 of 6)

- [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)
- [Computational Emotion Analysis From Images: Recent Advances and Future Directions](https://mlsanity.com/paper/2103.10798/) (2021)
- [A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications](https://mlsanity.com/paper/2311.11250/) (2023)
- [Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition](https://mlsanity.com/paper/2501.15519/) (2025)
- [Multimodal Sentiment Analysis: Perceived vs Induced Sentiments](https://mlsanity.com/paper/2312.07627/) (2023)

## Abstract (arXiv)

Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to sentiment analysis of textual data, there has been very limited work that focuses on analyzing sentiment of image data. In this work, we propose a novel visual sentiment prediction framework that performs image understanding with Deep Convolutional Neural Networks (CNN). Specifically, the proposed sentiment prediction framework performs transfer learning from a CNN with millions of parameters, which is pre-trained on large-scale data for object recognition. Experiments conducted on two real-world datasets from Twitter and Tumblr demonstrate the effectiveness of the proposed visual sentiment analysis framework.