## 5 Experiment Results and Discussion  
After applying the method that we have developed, we present in this section the results obtained and we will analyze the conclusions that can be observed from them. The subject we are dealing with is the emotional response of the population against the arrival of COVID-19 during a period comprising the months from February 2019 until October 2019, and February 2020 until October 2020. The reason behind selecting this topic for our study can be summarized into three main ones: (a) It is a relevant subject which still affects our daily lives, (b) it has reached all continents, turning this topic into a global concern, and (c) it is a highly discussed subject for many reasons (counter-measures, mask policies, negationists, etc.). We chose the same period for the previous year in order to establish a baseline on which to measure variations. Using the Twitter Search API, we collected millions of tweets from different European capitals for that time period. In order to harvest geolocated tweets, we specified the coordinates (latitude and longitude) for each of the cities (_Berlin_, _London_, _Madrid_, _Rome_, _Paris_, and _Warsaw_), covering a 20 km radius. The filters through which we ran our initial user list are reflected in Table 4 :  
Figure 4: Dimensional models of emotion. (a) Circumplex Model of Affect [28]; (b) PAD tri-dimensional space [29]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Location** & **User** & **Time** & **ID** & **V** & **A** & **D** \\ \hline Location1 & User1 & _period1_ & 0001 & 6.75 & 3.94 & 4.86 \\ Location1 & User1 & _period1_ & 0002 & 8.01 & 5.32 & 6.12 \\ Location1 & User1 & _period2_ & 0003 & 5.43 & 6.70 & 5.21 \\ Location1 & User2 & _period1_ & 0010 & 5.67 & 7.41 & 4.35 \\ Location1 & User2 & _period2_ & 0011 & 2.34 & 3.55 & 7.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sample Emotional Rating. We chose these locations as the target for our study based on the fact that, even if all of them belong to the same continent and the European Union, different measures were taken in each place, so we will observe similarities and differences in their reaction to the pandemic. Our Data Collection module contains the six harvesters in charge of building our dataset, based on previously defined criteria. No search terms have been specified, aiming to avoid a bias or polarization of the results, intending, therefore, to observe a general reaction, instead of a focused analysis around a specific topic. The resulting dataset is made up of more than 5 million tweets, coming from an average of 200 users per city. As we have already explained, these users will be the representative group at the geographical level for each city and country. We want to indicate the fact that no language filters have been imposed on the extraction of tweets, assuming that the vast majority of users would interact in the native language. Therefore, we used the Machine Translation service from Google Cloud API to translate all interactions into English, and the spaCy (spaCy [[https://spacy.io/](https://spacy.io/)]([https://spacy.io/](https://spacy.io/)), accessed on 26 February 2021) library to carry out the necessary lemmatization. To calculate the _Searching Behavior Index_ related to the subject of the COVID-19 pandemic, we chose the terms presented in Table 5. Each of these terms is language-specific for each country, following the assumption of a native-language search for each of the locations. To the best of our knowledge, there has not been any similar study that we can use as a comparison of ground truth. However, we extend the work done in the past [19], which covered the quantification of the Emotional Impact of certain events on different locations, by introducing the _degree of Emotional Concern_, which reflects the emotional response as the variation in people's concern based on values of valence, arousal, and dominance, and their relation to searching behavior on Google.