Word-level emotion distribution with two schemas for short text emotion classification

Abstract 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.