(CH-SIMS) Chinese Multimodal Sentiment Analysis Dataset

Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unifified multimodal annotations. However, the unifified annotations do not always reflflect the independent sentiment of single modalities and limit the model to capture the difference between modalities. In this paper, we introduce a Chinese single- and multimodal sentiment analysis dataset, CH-SIMS, which contains 2,281 refifined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis. Furthermore, we propose a multi-task learning framework based on late fusion as the baseline. Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations. The full dataset and codes are available for use at https://github.com/ thuiar/MMSA.