Hierarchical state recurrent neural network for social emotion ranking

Abstract

Text generation with auxiliary attributes, such as topics or sentiments, has made remarkable progress. However, high-quality labeled data is difficult to obtain for the large-scale corpus. Therefore, this paper focuses on social emotion ranking aiming to identify social emotions with different intensities evoked by online documents, which could be potentially beneficial for further controlled text generation. Existing studies often consider each document as an entirety that fail to capture the inner relationship between sentences in a document. In this paper, we propose a novel hierarchical state recurrent neural network for social emotion ranking. A hierarchy mechanism is employed to capture the key hierarchical semantic structure in a document. Moreover, instead of incrementally reading a sequence of words or sentences as in traditional recurrent neural networks, the proposed approach encodes the hidden states of all words or sentences simultaneously at each recurrent step to capture long-range dependencies precisely. Experimental results show that the proposed approach performs remarkably better than the state-of-the-art social emotion ranking approaches and is useful for controlled text generation.