{'fraction_non_alphanumeric': 0.027640671273445213, 'fraction_numerical': 0.014807502467917079, 'mean_word_length': 5.3375, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Emotional Reaction Analysis based on Multi-Label Graph Convolutional Networks and Dynamic Facial Expression Recognition Transformer

Automatically predicting and understanding human emotional reactions have wide applications in human-computer interaction. In this paper, we present our solutions to the MuSe-Reaction sub-challenge in MuSe 2022. The task of this sub-challenge is to predict the intensity of 7 emotional expressions from human reactions to a wide range of emotionally evocative stimuli. Specifically, we design an end-to-end model, which is composed of a Spatio-Temporal Transformer for dynamic facial representation learning and a multi-label graph convolutional network for emotion dependency modeling.We also explore the effects of a temporal model with a variety of features from acoustic and visual modalities. Our proposed method achieves mean Pearson's correlation coefficient of 0.3375 on the test set of MuSe-Reaction, which outperforms the baseline system(i.e., 0.2801) by a large margin.