Emotion Recognition in Context

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. Source: Context Based Emotion Recognition using EMOTIC Dataset

Yu Luo, Jianbo Ye, Reginald B. Adams, Jr., Jia Li, Michelle G. Newman and James Z. Wang, ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild,'' International Journal of Computer Vision, vol. 128, no. 1, pp. 1-25, 2020.

Jiyoung Lee, Seungryong Kim, Sunok Kim, Jungin Park, Kwanghoon Sohn; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 10143-10152

Emotional Dialogue Acts data contains dialogue act labels for existing emotion multi-modal conversational datasets. We chose two popular multimodal emotion datasets: Multimodal EmotionLines Dataset (MELD) and Interactive Emotional dyadic MOtion CAPture database (IEMOCAP). EDAs reveal associations between dialogue acts and emotional states in a natural-conversational language such as Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, and Thanking with Joy.

13,201 clips from 79 TV shows. Each video clip was manually annotated with six emotion categories, including “anger”, “disgust”, “fear”, “happy”, “sad”, and “surprise“, as well as “neutral”.

KD-EmoR is socio-behavioral emotion dataset for emotion recognition in realistic conversation scenarios. It consists of total 12289 sentences from 1513 scenes of a Korean TV show named 'Three Brothers'. The dataset is split into Training and testing sets. Each sample consists of sentence_id, person(speaker), sentence, scene_ID, context(Scene description) labeled with one of the following complex emotion labels: euphoria, dysphoria and neutral. This dataset can be used to study Emotion recognition in Korean conversations.