Distributed Representations of Emotion Categories in Emotion Space (2021)

Abstract: Emotion category is usually divided into different ones by human beings, but it is indeed difficult to clearly distinguish and define the boundaries between different emotion categories. The existing studies working on emotion detection usually focus on how to improve the performance of model prediction, in which emotions are represented with one-hot vectors. However, emotion relations are ignored in one-hot representations. In this article, we first propose a general framework to learn the distributed representations for emotion categories in emotion space from a given emotion classification dataset. Furthermore, based on the soft labels predicted by the pre-trained neural network model, we derive a simple and effective algorithm. Experiments have validated that the proposed representations in emotion space can express emotion relations much better than word vectors in semantic space.

## introduction in the past decades, a lot of tasks have been proposed in the field of text emotion analysis. the most primary one among them is emotion classification task @xcite . based on emotion classification task, many new tasks have been proposed from different considerations. @xcite proposed the task of emotion cause extraction, which aims at predicting the reason of a given emotion in a document. based on the emotion cause extraction task, @xcite introduced the emotion-cause pair extraction task for the purpose of extracting the potential pairs of emotions and corresponding causes in a document. @xcite proposed a target-dependent emotion recognition task, which aims at predicting the sentiment with the given query. to express the intensity of a specific emotion in text, @xcite proposed the emotion intensity detection task. however, all the above tasks treat emotions as independent ones and represent emotions with one-hot vectors, which definitely ignore the underlying emotion relations. based on existing emotion detection tasks, many efforts have been made to achieve better performance @xcite @xcite @xcite and many datasets have been introduced to train and evaluate the corresponding models @xcite @xcite . the vast majority of existing emotion annotation work assumes that the emotions are orthogonal to each other and represent the emotion categories with one-hot vectors @xcite @xcite . actually, the boundaries as well as the relations among emotion categories are not clearly distinguished and defined. typical word embedding learning algorithms only use the contexts but ignore the sentiment of texts @xcite . to encode emotional information into word embedding, sentiment embedding and emotion(al) embedding have been proposed @xcite @xcite . @xcite proposed a learning algorithm dubbed sentimentspecific word embedding (sswe). @xcite proposed a method to learn emotionenriched word embedding (ewe). however, all the above algorithms represent emotions in semantic space rather than emotion space. as shown in ## methodology in this section, we describe how to learn the distributed representations for emotion categories. first, a general framework is proposed. then, a simple and effective algorithm is derived based on the soft labels from a pre-trained neural network model. after that, we extend our method to multilabel datasets. at last, detailed approaches of the algorithm are listed. ## conclusion and future work in this paper, we argued that the emotion categories are not orthogonal to each other and the relations among emotion categories are very complex. we proposed a general framework to learn the distributed representation for each emotion category in emotion space from a given emotion dataset. then, a simple and effective algorithm was also derived based on the soft labels predicted by the pre-trained neural network model. we conducted three experiments to validate the effectiveness of our emotion representations and the experimental results demonstrated that our representations in emotion space can express emotion relations much better than representations from word embeddings. there are three avenues of future work we would like to explore. first, the distributed representations for emotion categories are derived from a specific emotion classification dataset. it would be interesting to build a universal emotion representation that is irrelevant to a specific corpus. second, the computation of our emotion representations relies on the soft labels predicted by the neural network model, and we would like to investigate a more general method in the future. finally, we would like to explore more nlp applications of our emotion rep-resentations, such as improving the performance of emotion classification models and studying emotion spaces across languages.