# A New Feature Selection Scheme for Emotion Recognition from Text
## 1 Introduction  
Emotion recognition has become a challenging problem in the field of natural language processing since there is a large amount of data stored on the Internet. Different methods have been developed and applied to recognize emotions in various applications such as image [1], speech [2], video [3], and text [4]. Moreover, understanding emotions may increase the success of robots in the applications where there is an interaction between humans and machines [5]. However, it is computationally expensive and hard to recognize emotions in images and videos. Therefore, in this paper, we focus on emotion recognition from text as it has become significantly important while the quantity of text documents has increased dramatically on the Internet after the arrival of the new millennium.  
Social networks via Internet based applications have become very popular where people convey their emotions through text [6; 7; 8]. Text classification is the task of classifying documents or data into predefined categories based on the context of content [9]. Classifiers perform the objective of classifying the document under labels close to the meaning of the content for easy recognition. They simply emulate human ability to classify a document, but it is done faster and accurately on massive information. Similarly, human emotion can be deduced from text through the application of text classification since the emotion expressed by humans can be categorized into different classes such as anger, joy, disgust, sadness, fear, and surprise [4]. This is not an exhaustive list [10]. Other emotions can be classified as secondary or tertiary forms of the six-basic forms [11]. Anger is considered as an undesirable (negative) emotion while Joy can be considered as a desirable (positive) emotion [12]. According to [PERSON] [13],there are three major monoamine systems which are serotonin, dopamine, and noradrenaline that have a great impact on emotions. [PERSON] developed a comprehensive theory of fundamental emotions and classified eight different emotions [10; 14; 15]. [PERSON] described them as basic emotions which stand for the strictly biological portion of emotion [10; 14]. These basic or fundamental emotions are enjoyment/joy, interest/excitement, surprise, anger/rage, disgust, distress/anguish, fear, and shame/humiliation. Note that, several of these emotions are created based on the character or personality of the individual whereas the rest of them are formed essentially related to the social interactions. Further details of psychological effects (interior and exterior) for the individual's emotions can be found in [10; 16; 17; 18]. In this work, seven basic emotion categories have been used for emotion recognition from text, which are described by the International Survey on Emotion Antecedents and Reaction [19]. These emotions are anger, disgust, fear, guilt, joy, shame, and sadness.  
There are several methods used in recognizing emotion from text. One of them is the lexicon based approach [20]. In a recent study, authors implemented emotion ranking using documents where each emotion is listed in decreasing order of magnitude. Additionally, they performed word-level emotion prediction using the proposed emotion lexicon generator [20]. An alternative approach is the learning-based method. In the learning-based method, the recently trained classifier which applies a specific machine learning model recognizes emotion from a new text input [21; 22; 23].