Multimodal hypergraph learning for microblog sentiment prediction

Abstract

Microblog sentiment analysis has attracted extensive research attention in the recent literature. However, most existing works mainly focus on the textual modality, while ignore the contribution of visual information that contributes ever increasing proportion in expressing user emotions. In this paper, we propose to employ a hypergraph structure to formulate textual, visual and emoticon information jointly for sentiment prediction. The constructed hypergraph captures the similarities of tweets on different modalities where each vertex represents a tweet and the hyperedge is formed by the “centroid” vertex and its k-nearest neighbors on each modality. Then, the transductive inference is conducted to learn the relevance score among tweets for sentiment prediction. In this way, both intra- and inter- modality dependencies are taken into consideration in sentiment prediction. Experiments conducted on over 6,000 microblog tweets demonstrate the superiority of our method by 86.77% accuracy and 7% improvement compared to the state-of-the-art methods.