https://arxiv.org/abs/1702.04280 DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network

This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system. The backbone of our system consists of several deep convolutional neural networks that are not only computationally inexpensive, but also provide state-of-the-art results on several competitive benchmarks. To power our novel deep networks, we collected large labeled datasets through a semi-supervised pipeline to reduce the annotation effort/time. We tested our system on several public benchmarks and report outstanding results.

https://arxiv.org/abs/1708.00524 Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

github.com/bfelbo/deepmoji

https://arxiv.org/abs/1708.04299 Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks

https://arxiv.org/abs/1608.05129v1 SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification

http://sunai.uoc.edu/emotic/index.html?lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BmfUVkrHKQdGNRtRen0W3bw%3D%3D EMOTIC Dataset

The EMOTIC dataset, named after EMOTions In Context, is a database of images with people in real environments, annotated with their apparent emotions. The images are annotated with an extended list of 26 emotion categories combined with the three common continuous dimensions Valence, Arousal and Dominance.

https://arxiv.org/abs/1806.09514v1 The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems