# A New Feature Selection Scheme for Emotion Recognition from Text
## 1 Introduction
### Related Work  
Before text classification takes place, the document set should go through several procedures. Firstly, the bag-of-words (BOW) representation allows each unique word in the document set to be handled as a distinct feature (or, term) [24]. Secondly, term scoring is applied in two critical areas which are term selection and term weighting. Term selection aims to select a subset of terms from the initial document collection to represent documents. Thus, it regards some terms as relevant and keeps them whereas it regards other terms as unnecessary and eliminates them by setting a threshold using numerous approaches [25; 26]. The reason is twofold. Firstly, using thousands of unique terms will lead to a very high dimensional feature space [26]. This will require extra storage, memory space, and computational power [27]. Secondly, the existence of non-informative words might produce a negative impact on the decision of the automatic categorization systems. After selecting a subset of words, quantification of the relative influence of the selected words is named as term weighting. In the field of emotion recognition from text, the BOW representation produces extremely sparse vectors due to small document lengths because text documents are made of a few number of sentences. This is named as the feature sparseness problem. The feature sparseness problem reduces the performance of the classifiers [28; 29]. A previous study constructed feature space using both single words and term sets and introduced a new term weighting method to weight term sets by utilizing feature similarities to solve the feature sparseness problem [30]. In a recent study, the BOW representation has been enriched using semantic and syntactic relations between words to enhance classification performance [26]. This enrichment has improved the recall of several classifiers by reducing sparseness. The authors state that short text documents are made of a few words which turn into high dimensional sparse vectors due to the excessive number of distinct terms as a result of a high number of training samples in many domains [28]. Another related research work concluded that rare words are significant and cannot be ignored because they produce better Accuracy scores as compared to the frequent terms if used in the classifiers [31]. The authors made their experiments in the field of patent classification and technical terms that are rare improved the performance [31]. [PERSON] et al. [32] proposed a supervised machine learning approach to classify emotions from text. The method uses a variation of Winnow update rule with different configurations of features. The authors state that it is significantly important to select the best feature set to increase the classification Accuracy score of emotion recognition. [PERSON] et al. [33] proposed an approach using large-scale real-world knowledge for emotion classification from text. The method has been applied to classify six different emotions which are happy, disgust, sad, fear, angry, and surprise. In [34], a hierarchical method is developed for emotion recognition from text. In order to achieve good results, authors used two different categories to classify the emotions which are positive (which represents the happiness emotion) and negative (which represents the other five emotions sadness, fear, anger, surprise, and disgust). The obtained results demonstrate that the approach performs better than the other classifiers. Moreover, in [35], another hierarchical approach is presented for emotion classification which is applied to Chinese micro-blog posts. [PERSON] et al. [36] developed a new feature extraction technique which is a knowledge-based topic model (KTM) to classify implicit emotion features. The SVM classifier has been applied to the extracted features to classify 19 different emotions from text. Experimental results show that authors achieved good results. [PERSON] et al. [37] proposed a new feature extraction technique named the unigram mixture model (UMM) and compared it with the BOW.