# Emotion Recognition Method for Call/Contact Centre Systems
## 2 Related Work  
With the steady popularization of conversation-based services, automatic recognition of human emotions using computer systems has become an important research trend. This is evidenced by the large number of review papers describing the most interesting systems for automatic recognition of emotions in voice data [8; 9; 10] and automatic recognition of emotions in text (usually comments left by users in various web applications) [11; 12; 13; 14]. These solutions are often systematized according to the emotion model adopted, the feature extraction method, and the classification algorithm used. To implement the emotion recognition system, emotion models must be defined and specified. So far, however, there have been difficulties in defining the term emotion, which is still an open problem in psychology. An attempt to solve it, in terms of the problem of CC systems, was made through research [15]. Two types of emotion models--categorical and dimensional--are the most commonly used. The categorical model classifies emotions as a fixed number of discrete categories. In contrast, the dimensional model describes an emotion as a point in a continuous multidimensional space. Many works use the Ekman model [16] or its derivatives. Recognition of emotions in text is often used in systems that allow analysis of comments left by users. These systems mostly boil down to sentiment analysis, which, as mentioned, involves assigning one of three categories to a given entry: positive affect, negative affect, and neutral attitude. The study of sentiment allows for a simplified classification that does not assume the presence of different types of emotions, but only a greatly simplified model. For many applications focused on assessing user satisfaction or evaluating user preferences, it is perfectly adequate [17]. There are known applications where a further reduction of the problem into positive and negative categories only is made without separating the neutral opinion [18]. Sentiment analysis has been repeatedly performed on data extracted from platforms such as Twitter [19; 20] and Facebook [21]. Well known methods used in sentiment analysis include deep learning, in particular using convolutional neural networks (CNN) [22] and networks using long short term memory (LSTM) [23]. Popular solutions also include works using both CNN and LSTM [24]. The paper [19] uses team learning methods to assess sentiment. The paper [14] uses a fuzzy system to assess sentiment. The combination of fuzzy system along with neural network-based models is presented in [12]. More specific methods include Spider Monkey hybrid optimization [25] or models based on rule systems [26]. An approach based on unsupervised learning can be found in paper [27]. Yet all of the above solutions apply only to sentiment assessment. However, in many applications requiring assessment of the user's emotional state, the use of sentiment analysis is not sufficient. The paper [19] proposes to extend the sentiment model to five states (strong positive, positive, neutral, negative, strong negative). The paper [26] distinguishes seven emotional states (fun, happiness, love, neutral, anger, hate, sadness), while the paper [28] distinguishes 27 different emotional states. In order to classify sentiment or emotional states, it is necessary to transform text into features suitable for use in machine learning models. Among the most popular methods are word embedding techniques such as BERT (bidirectional encoder representations from transformers.) [29] or GloVe (global vectors for word representation) [19]. The use of POS (part-of-speech tagging) has been proposed in papers [18; 19; 21]. The paper [22] uses the bag-of-words technique. Term frequency and inverse document frequency techniques were used in [19; 30]. The first part of Table 1 summarizes selected solutions for emotion recognition in text. In designing an emotion recognition system based on speech signal, one of the more challenging tasks is to identify and extract various emotion-related speech features.