# Sentiment-Aware Word Embedding for Emotion Classification
## 2 Related Work  
The study of emotional analysis is roughly summed up into three categories: Rule-based analysis, unsupervised classification, and supervised classification. Rule-based analysis is mainly performed together with the emotion lexicon. In English text analysis, [PERSON] proposed a distance measurement method to determine the semantic polarity of adjectives based on the synonym graph theory model. In the analysis of Chinese text, [PERSON] et al. introduced a simple method based on HowNet Chinese lexicon to determine the semantic direction of Chinese words [15]. [PERSON] et al. identified six kinds of emotion expressed by [PERSON] with the lexicon-based method [16]. However, the lexicon-based method is low in accuracy, and the classifying quality is easily limited by the lexicon [17]. In particular, the lexicon-based method ignores contextual information. Unsupervised classification analysis does not use tagged documents but relies on a documentation's statistical properties, NLP processes, and existing vocabulary, which has an emotional or polarizing tendency. [PERSON] presented a simple, unsupervised learning algorithm for classifying reviews [18]. [PERSON] and [PERSON] proposed a novel, probabilistic modeling framework based on latent Dirichlet allocation (LDA), called the joint sentiment/topic model (JST), which detects sentiments and topics simultaneously from the text [19]. They also explored various ways to obtain prior information to improve the accuracy of emotional detection. [PERSON] and [PERSON] [20] proposed a novel feature weighting approach for the sentiment analysis of Twitter data and a fine-grained feature clustering strategy to maximize the accuracy of the analysis. The analysis based on supervised classification generates an emotion classification model with labeled training data. The effectiveness of supervised technologies depends on the features used in the classification task. The bag-of-words features and their weighting scheme are widely used in natural language processing, which provides a simplified representation of documents through various features. However, these methods have limitations in the task of emotional analysis. Word embedding drives the development of many NLP tasks through the low-dimensional continuous vector representations of words. In the framework of the word embedding model, the word vector is generated according to the distribution hypothesis [12]. It has been found that learning vectors can clearly encode many linguistic regularities and patterns [21]. However, it is still not enough to rely solely on word-level distribution information collected from the text corpus to learn high-quality representations [22; 23]. Auxiliary information has been shown to help learn task-specific word embedding to improve the performance in the tasks [24; 25]. In order to improve the representation of word embedding, some research work has been proposed to incorporate various additional resources into the learning framework of word representation. Some knowledge-enhancing word embedding models incorporate lexical knowledge resources into the training process of word embedding models [22; 23; 26; 27]. In the study of [PERSON] and [PERSON] [28], the grammatical context of the automatically generated dependency analysis tree was used for word representation training. Meanwhile, some people learn cross-language word embedding with a multilingual parallel corpus [29; 30; 31]. [PERSON] and [PERSON] pointed out that background knowledge is composed of keywords and co-occurring words that are extracted from the external corpus and proposed a background knowledge-based multi-stream neural network [32]. Many effective analysis algorithms take advantage of existing knowledge to improve classification performance [33; 34; 35]. [PERSON] et al.