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.

具有主题或情感等辅助属性的文本生成已取得显着进步。但是，对于大型语料库，很难获得高质量的标签数据。因此，本文着重于社会情感排名，旨在识别在线文档引起的不同强度的社会情感，这可能对进一步控制文本生成有潜在的好处。现有研究经常将每个文档视为一个整体，无法捕获文档中句子之间的内部关系。在本文中，我们提出了一种用于社会情感排名的新型分层状态递归神经网络。采用层次结构机制来捕获文档中的关键层次语义结构。此外，与传统的递归神经网络中的增量式阅读单词或句子的序列不同，该方法在每个递归步骤同时对所有单词或句子的隐藏状态进行编码，以精确地捕获远程依存关系。实验结果表明，该方法的性能明显优于最新的社会情感排名方法，可用于控制文本的生成。