Semi-Supervised Dimensional Media Sentiment Analysis via Exploring Sample Relationships

Dimensional sentiment analysis (DSA) aims to recognize continuous real-valued annotations in multidimensional spaces such as valence-arousal space. It can serve as a sentiment analysis that is more fine-grained than traditional polarity classification. Existing methods primarily focused on supervised learning, which requires a large number of training samples. To address this issue, recent studies have suggested the use of a generative model for semi-supervised DSA. However, these methods lack the exploration of relationships between samples, which may limit their effectiveness. In this study, we proposed a sample relationships (SRs) model for semi-supervised DSA. Compared with the conventional methods, the SR module can integrate the interactive information from different samples, effectively capturing their interdependencies. The results of experiments conducted on three datasets indicated that the performance of the proposed method was better than those of the existing models and that the performance was effective and competitive even with insufficient data.