Cross-Modality Microblog Sentiment Prediction via Bi-Layer Multimodal Hypergraph Learning

Microblog sentiment prediction has attracted extensive research focus with wide application prospects. With the increasing proportion of multimodal tweets consisting of images, texts, and emoticons, new challenges have been raised to the existing sentiment prediction schemes. More crucially, it remains an open problem to model the dependency among multiple modalities, where one or more modalities may be missing. In this paper, we present a novel Bi-layer Multimodal Hypergraph learning (Bi-MHG) toward robust sentiment prediction of multimodal tweets to tackle the above challenges. In particular, we design a two-layer structure for the proposed Bi-MHG model: The first layer, that is, a tweet-level hypergraph, learns the tweet-feature correlation and the tweet relevance to predict the sentiments of unlabeled tweets. The second layer, that is, a feature-level hypergraph learns the relevance among different feature modalities (including the midlevel visual features in Sentibank 1) by leveraging prior multimodal sentiment dictionaries. These two layers are connected by sharing the relevance of multimodal features in a unified bilayer learning scheme. In such a way, Bi-MHG explicitly models the modality relevance rather than implicitly weighting multimodal features adopted in the existing Multimodal Hypergraph learning 2. Finally, a nested alternating optimization is further proposed for Bi-MHG parameter learning. We have carried out extensive evaluations on a real-world microblog dataset crawled from Sina Weibo. For the task of multimodal sentiment prediction, superior performance is reported over several state-of-the-art and alternative approaches, which demonstrates the merits of the proposed scheme.