Textual emotion analysis is a challenging research topic in the field of natural language processing (NLP), which plays an important role in related NLP tasks, such as opinion mining and personalized recommendation. Existing research on emotion analysis has focused mostly on detecting types of emotions, and has solved problems using classification-based methods. Recently, fine-grained emotion analysis has attracted the attention of researchers for probing the essential elements of emotions, such as the causes, experiencers and results of emotion events, which could help further elucidate textual emotions in more depth. In this paper, we focus on the task of emotion cause extraction, aiming to recognize the causes in sentences that provoke certain emotions. We propose a two-stage supervised ranking method for accurately extracting the emotion causes based on information retrieval techniques. In the first stage, we measure the complexity of provoked emotions using query performance predictors to distinguish the number of causes for each emotion in contexts. In the second stage, we incorporate the emotion complexity into learning an autoencoder-enhanced ranking model for accurately extracting the causal clauses. We also extract abundant emotion-level clause features for clause representations as the learning samples. We evaluate the proposed method on an existing dataset for emotion cause extraction and demonstrate that our method significantly outperforms the state-of-the-art baseline methods. The proposed method is effective in extracting textual emotion causes in sentences, which can greatly benefit in-depth emotion analysis for effective cognitive computing.

文本情感分析是自然语言处理（NLP）领域的一个具有挑战性的研究课题，它在相关的 NLP 任务中扮演着重要的角色，例如意见挖掘和个性化推荐。现有的情绪分析研究主要集中在检测情绪类型，并使用基于分类的方法解决了问题。近来，细粒度情感分析引起了研究人员的关注，它探索情感的基本要素，如情感事件的原因、经历者和结果，有助于更深入地阐明文本情感。在本文中，我们专注于情感原因提取的任务，旨在识别引起某些情感的句子中的原因。我们提出了一种基于信息检索技术准确提取情感原因的两阶段监督排序方法。在第一阶段，我们使用查询性能预测器来衡量激发情绪的复杂性，以区分上下文中每种情绪的原因数量。在第二阶段，我们将情感复杂性纳入学习自动编码器增强的排序模型，以准确提取因果子句。我们还为子句表示提取了丰富的情感级子句特征作为学习样本。我们在用于情感原因提取的现有数据集上评估了所提出的方法，并证明我们的方法明显优于最先进的基线方法。所提出的方法在提取句子中的文本情感原因方面是有效的，