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 support a deeper understanding of the emoji modality and their usage on social media.

本研究提出了一种基于与误码相关的大规模数据集的情感定位图像嵌入方法，在Twitter数据集上使用表情符号来训练深度神经模型，有效地解决了对象分类模型在情感分析中的限制，其结果在公共情感分析基准测试中表现出优秀的性能。同时，研究者还基于表情符号的视觉情感反应提出了一种新的表情符号表示方法，加深了对表情符号模态的理解。

面带微笑，心情愉悦 :) 基于 Emoji 的视觉情感分析