# Sentiment-Aware Word Embedding for Emotion Classification
###### Abstract  
Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating sentiment evidence within the emotional embedding component of a term vector. We take advantage of the multiple types of emotional knowledge, just as the existing emotional lexicon, to build emotional word vectors to represent emotional information. Then the emotional word vector is combined with the traditional word embedding to construct the hybrid representation, which contains semantic and emotional information as the inputs of the emotion classification experiments. Our method maintains the interpretability of word embeddings, and leverages external emotional information in addition to input text sequences. Extensive results on several machine learning models show that the proposed methods can improve the accuracy of emotion classification tasks. emotion classification; sentiment lexicon; text feature representation; hybrid vectorization; sentiment-aware word embedding +
Footnote †: journal:  
Text consists of many ordered words in the emotional analysis task. In order to process a text document mathematically, the text document is projected into the vector space. A text document is represented by a vector in the same vector space so that the document can be classified by a model. The BoW (bag-of-words) model is widely used in text processing applications. It is an approach to modeling texts numerically [1]. It processes texts regardless of word order and semantic structure and disregards context which means that it is unable to sufficiently capture complex linguistic features. At the same time, one of the drawbacks of BoW is its high number of dimensions and excessive sparsity. The appearance of word embedding overcomes this shortcoming. Recently, word embedding based approaches [2; 3] have learned from low-dimensional, continuously-valued vector representations using unsupervised methods over the large corpus. State-of-the-art word embedding algorithms include the C and W model [4], the continuous bag-of-words (CBOW) model, the Skip-Gram Word2 Vec model [3], and the GloVe (Global Vectors for Word Representation) model [5]. Word embedding techniques have been shown to facilitate a variety of NLP (natural language processing) tasks, including machine translation [6], word analogy [7], POS (part of speech) tagging [8], sequence labeling [4], named entity recognition [9], text classification [10], speech processing [11], and so on. The principle behind these word embedding approaches is the distributional hypothesis that \"You shall know a word by the company it keeps\" [12]. By leveraging statistical information, such as word co-occurrence frequencies, the method could explicitly encode many linguistic regularities and patterns into vectors. It produces a vector space in which each unique word in the corpus is assigned a corresponding vector in the space, and words with similar contexts in the training corpus are located in close proximity. The feasibility of distribution assumptions has been confirmed in many experiments [13; 14]. However, most existing word embedding algorithms only consider statistical information from documents [3; 5; 7]. The representations learned from these algorithms are not the most effective for emotional analysis tasks. In order to improve the performance of word embedding in emotional analysis tasks, a method that combines the traditional word embedding and provides prior knowledge from external sources is proposed. The knowledge of the polarity and intensity of emotional words can be obtained via public sentiment lexicons, and this sentiment information is not directly obtained in word co-occurrence frequencies, which can greatly enhance the performance of word embedding for emotional analysis.