Emotion Classification by Jointly Learning to Lexiconize and Classify (2020)

Abstract: Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional network (EmNet) to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. The dynamic emotion lexicons are useful for handling words with multiple emotions based on different context, which can effectively improve the classification accuracy. We validate the approach on two representative architectures – LSTM and BERT, demonstrating its superiority on identifying emotions in Tweets. Our model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.

## introduction tweet emotion this is a joke really how long will he keep diving and ducking. disgust that's the joke. i know it's incense. joy the last several years have seen a land rush in research on identification of emotions in short text such as twitter or product reviews due to its greatly commercial value. for example, the emotions (e.g., anger or joy) expressed in product reviews can be a major factor in deciding the marketing strategy for a company @xcite . the sota approaches to this task @xcite generally employ pre-defined emotion lexicons, which have two major limitations: in this work, we tackle these challenges by jointly learning to construct the emotion lexicons and classify the emotions of short texts. specifically, we propose a novel emotional network (emnet), which consists of three main components: ## related work emotion classification has been extensively studied due to its wide applications in recent years. different from sentiment classification which can be treated as either a single-label classification task (e.g., positive, negative), emotion classification or affect detection is a multi-label classification task which is to detect a discrete set of emotions present in a given sentence such as anger, joy, sadness etc @xcite . traditional methods such as lexicon, n-gram and graph models have been used. @xcite proposed a coarse-to-fine strategy for multi-label emotion classification. they dealt with the data sparseness problem by incorporating the transfer probabilities from the neighboring sentences to refine the emotion categories. @xcite recast multi-label emotion classification as a factor graph inferring problem in which the label and context dependence are modeled as various factor functions. yan and turtle (2016) the neural network models have also been used in emotion classification. for example, @xcite proposed an emotion distribution learning (edl) method, which first used recursive autoencoders (raes) to extract features and then conducted multi-label emotion classification by incorporating the label relations into the cost function. @xcite provided an end-to-end learning framework by integrating representation learning and multi-label classification in one neural network. recently, external knowledge has been widely employed for this task. one representative research line is the transfer learning. @xcite proposed a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. they transferred the sentiment classification knowledge to emotion classification tasks. @xcite proposed a bi-lstm architecture equipped with a multi-layer self attention mechanism. the attention mechanism can identify salient words in tweets, as well as gain insight from the models and make them easier to interpret. they leveraged a small scale annotated emotion dictionary and treated the annotated as fixed word affective features. they achieved the highest accuracy in semeval 2018 workshop. though the external dictionaries are effective, they are always task specific, different in granularity and limited in scale. different from these work, we propose an emotion network to jointly learn emotion lexicons and classification. the learned emotion lexicons depend on a certain context, which are effective for emotion disambiguation and avoid the domain mismatch problem. ## conclusion and future work in this paper, we propose a novel emotion network. our model can jointly learn word emotions and conduct classification. we apply the emotion network to both lstm and bert models. experimental results on the public twitter dataset show that our model can learn reasonable word emotions, which can boost the classification and achieve significant improvements over several baseline models. further analyses demonstrate the effectiveness of the proposed methods and intuitively interpret how our model works. in future work, along this research direction, we will try to apply our method to other tasks such as aspect-level classification to verify the effectiveness.