Improving multilabel text emotion detection with emotion interrelation anchors

Emotion detection studies the problem of automatic identification of emotions expressed in text. Since multiple emotions may co-occur in a single text excerpt, state-of-the-art approaches often cast this multi-label classification task to multiple, independent binary classification tasks, each specialized for one emotion class. The main disadvantage of such approaches is that, by design, each binary classifier overlooks typical emotion interrelationships, such as co-occurrence (e.g., anger and fear) or mutual exclusiveness (e.g., sadness and joy). This paper proposes a simple and lightweight approach to re-introduce emotion interrelations into each binary classification task, where each binary classifier is able to understand the presence of other emotions, without directly inferring them. This is achieved by incorporating the proposed emotion anchors (i.e. features of representative emotional phrases) into the model of each binary classifier. More specifically, the model is trained to incorporate other emotions in its representation by learning the parameters of an attention mechanism. Based on experiments on multiple datasets, our approach improves emotion classification performance in both supervised and few-shot domain adaptation settings, outperforming standard binary models in terms of accuracy and macro averaged F1-scores. The approach is generic and can be applied to other interrelated multi-label binary classification tasks.