# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 2 Materials and Methods
### Review Methodology  
The research field was systematically determined as following the scientometric review methodology [38]:  
* First, the subject of the review was searched in the Scopus and WoS databases. The search string was designed according to the research topic of recommendation systems in the tourism domain based on recognizing emotions from wearable devices' physiological data. * Secondly, the scientometric tool ScientoPy [37] was used, which pre-processed these two bibliographic databases' files. In this way, several clusters were determined, and the categories related to the research topic were formed. Besides, the lead authors' first 1000 keywords were chosen from this dataset consisting of 1449 documents. Then, the most relevant author keywords from this list were analyzed to consolidate 16 categories (recommender system, tourism, emotion recognition, machine learning, social media, user modeling, collaborative filtering, mobile application, context, personalization, sentiment analysis, wearable, healthcare, ontology, affective computing, and physiological signal). Later, the categories presented in the graphics cluster the similar author keywords that belong to the same topic (such as words in plural/singular, acronyms, classes, or category types). For instance, the RS topic includes the keywords (recommender system, recommendation system, recommendation, recommendation systems, recommendations, and others), and the deep learning topic includes the keywords (convolutional neural networks, convolutional neural network, CNN, deep neural network, LSTM, and others). * Third, it shows the statistical graphs of the bar and parametric trend analysis constructed with the indicators of Average Documents per Year (ADY) and Percentage of \begin{table}
\begin{tabular}{l r r} \hline \hline
**Information** & **Number** & **Percentage** \\ \hline Total loaded documents & 1829 & \\ Omitted documents by type & 200 & 10.9\% \\ Total documents after omitted documents removed & 1629 & \\ Loaded documents from WoS & 547 & 33.6\% \\ Loaded documents from Scopus & 1082 & 66.4\% \\ \hline
**Duplication removal statics:** & \\ Duplicated papers found & 180 & 11.0\% \\ Removed duplicated papers from WoS & \\ Removed duplicated papers from Scopus & 180 & 16.6\% \\ Total papers after remove duplicates & 1449 & \\ Papers from WoS & 547 & 37.8\% \\ Papers from Scopus & 902 & 62.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Preprocess brief with ScientoPy for the dataset obtained from WoS and Scopus. Documents in Recent Years (PDLY) [37]. It is interesting to highlight the rise of the RS and tourism as transversal and thematic axes. Figure 2 shows the trend bar graph of the main categories and highlights in the orange bar the documents published in the last four years in sentiment analysis, wearable devices, physiological signals, and use of ML algorithms in the ER. Also, it includes the value of PDLY (2016-2019). Similarly, the trend analysis in Figure 3 uses the ADY and PDLY indicators to describe the behavior of the strongly related themes to SR-based research. The graph on the left shows the evolution of the S curve of technology or category calculated by the number of documents accumulated per year (logarithmic scale). It represents the initial evolution, the period of growth, and the boom of the publication of documents related to research topics. While the parametric scatter graph located on the right side visualizes the growth of publications in recent years (2016-2019). New themes have emerged to support tourism SR development, such as sentiment analysis, wearable devices, social networks, and ML algorithms. The thematic axes of ER, affective computing, and collaborative filtering are of great interest to recommenders. * Fourth, the trend analysis of research belonging to these clusters was carried out with the WoSViewer (Section 7) and ScientoPy tools, which determined that the boom in these clusters' publications began in 2016 (see Figures 2 and 3).