# An Explainable Artificial Intelligence Approach for Detecting Empathy in Textual Communication
## 4 Empathy Measurement
### Empathetic Conversations (EC)  
In order to detect empathy in texts, it was necessary for us to procure a database that contained empathetic exchanges. The database that was procured after the analysis of the various options [34; 35; 36] was EmpatheticDialogues (ED). This database was proposed by [PERSON] et al. [37; 38] with the express purpose of working as a repository for empathetic conversations. The database contains 24,850 multi-turn conversations grounded in situations prompted by specific emotion labels. The conversations were created by a group of participants by this given principle. The resulting exchanges in the database have been considered empathetic by human observers [38], and it has become the basis for a large number of research projects related to the generation of empathetic conversations using deep-learning [36; 39; 40; 41; 42]. In the presence of this fact, we consider this database very useful for the task of detecting empathy, as it has a history of being useful for related research. An important aspect of the database is the human metric for empathy/sympathy. [PERSON] et al. [38] describe the measurement of empathy through a Likert scale presented to the participants. The scale contains five points, and it goes by the following order: 1: not at all, 3: somewhat, 5: very much. The description of the metric is: \"Whether the responses show understanding of the feelings of the person talking about their experience\" [38]. The metric was sound and could be used as a base for this research. Since it was based on the Likert scale [43], it was substantiated in previous research of Affective variables [44]. Furthermore, sympathy as a special subset of empathy [18] is accounted for in the metric. However, there was an element of this metric that presented uncertainty: Since the database was created by the use of crowd-sourcing through Amazon Mechanical Turk (MTurk), we were not sure of the validity of each participant's empathetic judgment. People present different capabilities when it comes to carrying out and identifying empathetic behavior [18; 19; 45; 46]. For example, a lack of empathy is a feature in certain psychological conditions [20; 45; 46]. A person with high empathy will be proficient in all of its aspects [18; 46]. Therefore, they can provide a more reliable measurement of it. We cannot rule out that some of the measurements are inaccurate because of the empathetic abilities of the participants, or their state of mind during the evaluation. It is because of this that we were compelled to evaluate the empathy/sympathy scores present in the database. To do this, we would obtain the help of empathetic people through the use of the Empathy Quotient (EQ) [18; 20; 45; 46; 47]. Our group of participants would measure the empathy in a sample of conversations taken from ED. If the scores reported by the Empathy/Sympathymetric differed from the actual empathy found in the conversations according to our group, we would be forced to discard the scores from ED. The EQ is a method for measuring empathy in adults developed by [PERSON] and [PERSON] [18]. It consists of a self-report scale designed to measure empathy on a scale from zero to eighty [18]. [PERSON] and [PERSON] designed the EQ to be sensitive to a lack of empathy as a feature in psychopathology [20]. Based on this principle, validation studies on the EQ's reliability have shown that it is a useful method for measuring empathy in a single dimension [20; 45; 46; 47]. [PERSON] and [PERSON] postulate that there is a useful cut-off point of 30 that separates those that have trouble with empathy and those that were in the control group [18]. Based on this, we will consider as empathetic people those that are above this threshold. We carried out a process in order to evaluate the validity of the Empathy/Symptathy metric. It was necessary to obtain a panel of volunteers that scored favorably on the EQ in order to ensure that they were highly empathetic.