# Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis

## TL;DR

This paper introduces a sentiment-aligned image embedding learned from social media data using emojis, significantly improving visual sentiment analysis performance over traditional object-based embeddings.

## Contribution

It proposes a novel emoji-based image embedding trained on 4 million social media images, outperforming existing object-based methods in sentiment analysis tasks.

## Key findings

- Embedding outperforms object-based counterparts across benchmarks.
- Simple, compact embedding surpasses complex state-of-the-art models.
- New emoji representation based on visual emotional response enhances understanding.

## Abstract

Due to the lack of large-scale datasets, the prevailing approach in visual sentiment analysis is to leverage models trained for object classification in large datasets like ImageNet. However, objects are sentiment neutral which hinders the expected gain of transfer learning for such tasks. In this work, we propose to overcome this problem by learning a novel sentiment-aligned image embedding that is better suited for subsequent visual sentiment analysis. Our embedding leverages the intricate relation between emojis and images in large-scale and readily available data from social media. Emojis are language-agnostic, consistent, and carry a clear sentiment signal which make them an excellent proxy to learn a sentiment aligned embedding. Hence, we construct a novel dataset of 4 million images collected from Twitter with their associated emojis. We train a deep neural model for image embedding using emoji prediction task as a proxy. Our evaluation demonstrates that the proposed embedding outperforms the popular object-based counterpart consistently across several sentiment analysis benchmarks. Furthermore, without bell and whistles, our compact, effective and simple embedding outperforms the more elaborate and customized state-of-the-art deep models on these public benchmarks. Additionally, we introduce a novel emoji representation based on their visual emotional response which supports a deeper understanding of the emoji modality and their usage on social media.