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User emotion analysis toward videos is to automatically recognize the generalemotional status of viewers from the multimedia content embedded in the onlinevideo stream. Existing works fall in two categories: 1) visual-based methods,which focus on visual content and extract a specific set of features of videos.However, it is generally hard to learn a mapping function from low-level videopixels to high-level emotion space due to great intra-class variance. 2)textual-based methods, which focus on the investigation of user-generatedcomments associated with videos. The learned word representations bytraditional linguistic approaches typically lack emotion information and theglobal comments usually reflect viewers' high-level understandings rather thaninstantaneous emotions. To address these limitations, in this paper, we proposeto jointly utilize video content and user-generated texts simultaneously foremotion analysis. In particular, we introduce exploiting a new type ofuser-generated texts, i.e., "danmu", which are real-time comments floating onthe video and contain rich information to convey viewers' emotional opinions.To enhance the emotion discriminativeness of words in textual featureextraction, we propose Emotional Word Embedding (EWE) to learn textrepresentations by jointly considering their semantics and emotions.Afterwards, we propose a novel visual-textual emotion analysis model with DeepCoupled Video and Danmu Neural networks (DCVDN), in which visual and textualfeatures are synchronously extracted and fused to form a comprehensiverepresentation by deep-canonically-correlated-autoencoder-based multi-viewlearning. Through extensive experiments on a self-crawled real-worldvideo-danmu dataset, we prove that DCVDN significantly outperforms thestate-of-the-art baselines.

Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks