Understanding word-level emotion in terms of both category and intensity has always been considered an essential step in addressing text emotion classification tasks. Existing studies have mainly adopted the categorical lexicons that are tagged by predefined emotion taxonomies to link affective words with discrete emotions. However, in these lexicons, emotion tags are restricted to a specific set of basic emotions. Moreover, the emotional intensity is ignored, making these methods less flexible and less informative. This paper proposes a novel method to generate a word-level emotion distribution (WED) vector by incorporating domain knowledge and dimensional lexicon. The proposed method can link a word with more generic and fine-grained emotion taxonomies with quantitatively computed intensities. We propose two schemas to utilize the WED vector implicitly and explicitly to facilitate classification. The implicit approach implements a rule-based conversion strategy to augment the information in the label space. The explicit approach exploits WED as an emotional word embedding to enhance the sentiment feature. We conduct extensive experiments on seven multiclass datasets. The results indicate that both proposed schemas produce competitive results compared with the state-of-the-art baselines.

从类别和强度方面理解词级情感一直被认为是解决文本情感分类任务的重要步骤。现有研究主要采用由预定义的情感分类法标记的分类词典将情感词与离散情感联系起来。然而，在这些词典中，情感标签仅限于一组特定的基本情感. 此外，忽略了情绪强度，使这些方法不太灵活，信息量也较少。本文提出了一种通过结合领域知识和维度词典来生成词级情感分布 (WED) 向量的新方法。所提出的方法可以将单词与具有定量计算强度的更通用和细粒度的情感分类法联系起来。我们提出了两种模式来隐式和显式地利用 WED 向量来促进分类。隐式方法实现了基于规则的转换策略来增加标签空间中的信息。显式方法利用 WED 作为情感词嵌入来增强情感特征。我们对七个多类数据集进行了广泛的实验。