## 4 Results and Discussion  
We tested the proposed framework to analyze the emotions expressed by users for the 50 theaters located in the municipality of Naples (NA). All 50 theaters have been georeferenced as point features on the map. Subsequently, for each theater the reviews and opinions of users posted on a set of Italian websites dedicated to reviews of service facilities were acquired; in particular, reviews written on the websites TripAdvisor ([[https://www.tripadvisor.it/](https://www.tripadvisor.it/)]([https://www.tripadvisor.it/](https://www.tripadvisor.it/)) accessed on 1 April 2023) and Google Maps ([[https://www.google.it/maps/](https://www.google.it/maps/)]([https://www.google.it/maps/](https://www.google.it/maps/)) accessed on 1 April 2023) were acquired, considering the reviews inserted in the last five years. Four theaters (Tasso, Spazio Libero, Auditorium di Scampia, and Villa Patrizi) were closed during this period. They were georeferenced but were not evaluated. In Figure 2, the area of study is shown. The 46 analyzed theaters are shown on the map filled by the light green color. To analyze the relevance of the emotions, we use the sixteen primary and secondary emotion categories in [PERSON]'s wheel of emotions (Figure 3a). A secondary emotion category was obtained from the combination of two primary emotion categories, as displayed in the graph of the emotion primary dyads in Figure 3b where the secondary emotion categories are displayed as connecting arrows of two primary emotions. Table 2 shows the sixteen emotional categories where each emotional category is classified as primary/secondary and pleasant/unpleasant. Figure 3: [PERSON]’s wheel of emotions (**a**) and emotion primary dyads (**b**). Figure 2: Map of the area of study where the theaters are located. To create the emotional relevance fuzzy partition, we used a fuzzy partition composed of n = 7 fuzzy sets labelled as: _null_, _low_, _medium_-_low_, _medium_, _medium_-_high_, and _very_-_high_. The fuzzy set null is an R-function, the fuzzy set very-high is an L-function, and the other fuzzy sets are triangular fuzzy numbers. Figure 4 shows these seven fuzzy sets. Then, we constructed, in the Italian language, the dictionary of emotional words for the sixteen primary and secondary pleasant and unpleasant emotion categories in [PERSON]'s wheel. To construct the dictionary, for each emotional category, all the known terms associated with it were analyzed; subsequently, for each term, additional terms present in synonym dictionaries were selected. Each term in the dictionary was subsequently reduced to its inflectional form. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Emotional Category** & **Primary/Secondary** & **Pleasant/Unpleasant** \\ \hline Joy & Primary & Pleasant \\ Trust & Primary & Pleasant \\ Fear & Primary & Unpleasant \\ Surprise & Primary & Pleasant \\ Sadness & Primary & Unpleasant \\ Disgust & Primary & Unpleasant \\ Anger & Primary & Unpleasant \\ Expectation & Primary & Pleasant \\ Aggression & Secondary & Unpleasant \\ Optimism & Secondary & Pleasant \\ Love & Secondary & Pleasant \\ Submission & Secondary & Unpleasant \\ Disapproval & Secondary & Unpleasant \\ Contempt & Secondary & Unpleasant \\ Remorse & Secondary & Unpleasant \\ Awe & Secondary & Unpleasant \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Primary and secondary emotional categories in [PERSON]’s wheel of emotions. Figure 4: The emotional relevance fuzzy partition used in the tests. Finally, we executed our algorithm to classify the 46 spatial entities. For each of the sixteen pleasant and unpleasant emotional categories, the thematic map of the relevance of the emotional category was extracted. Each thematic class in the map represents the fuzzy relevance label assigned to the emotional category. In Figure 5, the eight thematic maps of the relevance of the primary emotional categories are shown.