# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 8 Discussion  
The key challenges identified in the evolution, trends, and co-relationships of SR in the domain of emotion-based tourism are described below. This paper provides an overview of the background, algorithmic approaches, data models, and emerging technologies involved in sentiment analysis and affective recognition. These guidelines for the design of tourist recommenders with affective contextual information are aimed at the academic and scientific community. First, challenging the emotional context leads to improved user experience and accuracy of travel recommenders. Initially, Section 3 analyzes the dominance of SR architecture approaches, platforms, and components [4; 13; 14; 50]. Table 3 chronologically summarizes some studies on CARS with data sources, user models, algorithms, similarity metrics, and performance evaluation. It shows that most of the works used sentiment analysis techniques to extract the emotional context of the users' comments posted on social networks. However, the collection of physiological data with wearable devices for emotional recognition in tourism has been little explored. Also, rural tourism emerges as an area of interest in planning personalized trips to manage geographical, emotional, and environmental factors. Additionally, both wearable, IoT, and Big Data technologies are emerging in smart tourism to implement recommenders of positive and satisfactory tourism experiences [22; 93; 94; 94]. Figure 9: It is a co-occurrence network mapping of author keywords related to the RS, tourism, ER, and ML. Also, it displays five color clusters made up of nodes identified by labels. The grouping of relevant documents defines the nodes’ size and the lines between the nodes. Second, the emotion recognition of Section 4 describes the framework for the analysis of physiological signals, affective detection, and validation of the classifier's results. The relationship between physiological changes and emotional models was evidenced, emphasizing [PERSON]'s circumflex [181]. In particular, the measurement of the dimensions of arousal and valence in the face of short-term stimulus elicitation in a controlled laboratory environment. Additionally, Table 4 chronologically summarizes some studies with the experimental design of the collection of affective data, extraction of features from physiological signals, and prediction algorithms. As a result, the detection of arousal achieved similar or better accuracy than the detection of valence [184; 181; 36]. However, in the tourism domain, emotions are considered a relevant contextual factor in the recommendation's satisfaction. For this reason, there is the challenge of proposing well-defined experimental designs to obtain physiological data and measurements of emotions in everyday life. Third, wearable technology and IoT environments have supported the infrastructure for data collection to personalize healthcare services [24; 25], music recommendations [185; 194], and suggestions of e-commerce products [185; 196]. In particular, Section 5 related recent studies of emotion recognition based on data from physiological sensors (see Table 4), recognition of human activity using inertial sensors [226; 30], and augmented reality applications supported on Smart devices Glasses [221; 222]. Besides, the investigations evidenced the correlation between emotions and data from the physiological sensors of Empatica E4 wristband devices (EDA and HR) [32], [PERSON] live smartwatch [12] (HRV) and electrodes (ECG, GSR, and EDA) [11; 36]. Hence, in the tourism domain, wearable sensors' integration could improve the recommenders' prediction by defining a user model with various contextual factors. Fourth, the ML approaches referenced in Section 3 and Section 6 were organized into classification, clustering, and Deep Learning Network (DLN) algorithms. First, the classification approaches in most of the studies described in Table 4 used classical ML algorithms based on feature extraction engineering (KNN, SVM, and RF).