# A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets
## 1 Introduction  
Emotions are the key to people's feelings and thoughts. Online social media, such as Twitter and Facebook, have changed the language of communication. Currently, people can communicate facts, opinions, emotions, and emotion intensities on different kinds of topics in short texts. Analyzing the emotions expressed in social media content has attracted researchers in the natural language processing research field. It has a wide range of applications in commerce, public health, social welfare, etc. For instance, it can be used in public health [1; 2], public opinion detection about political tendencies [3; 4], brand management [5], and stock market monitoring [6]. Emotion analysis is the task of determining the attitude towards a target or topic. The attitude can be the polarity (positive or negative) or an emotional state such as joy, anger, or sadness [7]. Recently, the multi-label classification problem has attracted considerable interest due to its applicability to a wide range of domains, including text classification, scene and video classification, and bioinformatics [8]. Unlike the traditional single-label classification problem (i.e., multi-class or binary), where an instance is associated with only one label from a finite set of labels, in the multi-label classification problem, an instance is associated with a subset of labels. Most previous work on sentiment and emotion analysis has only focused on single-label classification. Hence, in this article, we focus on the multi-label emotion classification task, which aims to develop an automatic system to determine the existence in a text of none, one, or more out of eleven emotions: the eight Plutchik [9] categories (joy, sadness, anger, fear, trust, disgust, surprise, and anticipation) that are shown in Figure 1, plus optimism, pessimism, and love. One of the most common approaches to addressing the problem of multi-label classification is the _problem transformation_. With this approach, a multi-label problem is transformed into one or more single-label (i.e., binary or multi-class) problems. Specifically, single-label classifiers are learned and employed; after that, the classifiers' predictions are transformed into multi-label predictions. Different transformation methods have been proposed in the multi-label literature. The most common method is called _binary relevance_[10, 11]. The idea of the binary relevance method is simple and intuitive. A multi-label problem is transformed into multiple binary problems, one problem for each label. Then, an independent binary classifier is trained to predict the relevance of one of the labels. Although binary relevance is popular in the literature, due to its simplicity, it suffers from directly modeling correlations that may exist between labels. However, it is highly resistant to overfitting label combinations, since it does not expect examples to be associated with previously-observed combinations of labels. In this article, we propose a novel transformation method, called \(xy\)-pair-set, for the multi-label classification problem. Unlike binary relevance methods, our method transforms the problem into only one binary classification problem as described in Section 3. Additionally, we exploit the successes of deep learning models, especially the word2 vecmethods' family [12] and the recurrent neural networks [13, 14] and attention models [15, 16], to develop a system that solves the transformed binary classification problem. The critical component of our system is the embedding module, which uses three embedding models and an attention function to model the relationship between the input and the label. Figure 1: The set of the eight basic emotions proposed by [PERSON] [9]. To summarize, the contribution of this work is four-fold. * We propose a novel transformation mechanism for the multi-label classification problem. * We propose a novel, attentive deep learning system, which we call Binary Neural Network (BNet), which works on the new transformation method.