## 3 The Proposed Method  
Our framework imports unstructured data inserted into reviews and comments posted on the Web that refer to N service facilities (called _spatial entities_) located in the area of study and uses the FREDoC model to assess the relevance of user emotions. For each spatial entity, the relevance of a set of M pleasant and unpleasant emotional categories is assessed; for this purpose, the dictionary of emotional words including in the stemmed form the terms belonging to each pleasant and unpleasant emotional category is created; in addition, the emotional relevance fuzzy partition is created. The emotional relevance fuzzy partition is constructed as a Ruspini fuzzy partition [33] on the domain [0, 1] of the normalized relevance values,  
Let {A\({}_{1}\), A\({}_{2}\),..., A\({}_{n}\)}, a family of n fuzzy sets defined in a domain X. It is a Ruspini fuzzy partition if the following constraints hold:  
\[\exists\mathrm{x}\in\mathrm{X}:\mathrm{A_{k}(x)}\
eq 0\quad\quad\mathrm{k}=1,2 \ldots,n \tag{4}\]  
\[\sum_{\mathrm{k}=1}^{n}\mathrm{A_{k}(x)}=1\forall\mathrm{x}\in\mathrm{X} \tag{5}\]  
The emotional relevance fuzzy partition on the domain [0, 1] is composed of a Ruspini fuzzy partition given by a family of n fuzzy sets given by trapezoidal fuzzy numbers, in the form:  
\[\mathrm{A_{k}(x)}=\left\{ \begin{array}{ll}0&\mathrm{x}<\mathrm{a_{k1}}\\ \frac{\mathrm{x}-\mathrm{a_{k1}}}{\mathrm{a_{k2}}-\mathrm{a_{k1}}}&\mathrm{a_ {k1}}\leq\mathrm{x}<\mathrm{a_{k2}}\\ 1&\mathrm{a_{k2}}\leq\mathrm{x}\leq\mathrm{a_{k3}}&\mathrm{k}=1,2,\ldots,n \quad\quad\mathrm{x}\in[0,1]\\ \frac{\mathrm{x}-\mathrm{a_{k1}}}{\mathrm{a_{k3}}-\mathrm{a_{k4}}}&\mathrm{a_ {k3}}<\mathrm{x}\leq\mathrm{a_{k4}}\\ 0&\mathrm{x}>\mathrm{a_{k4}}\end{array} \right. \tag{6}\]  
where \(0<\mathrm{a_{k1}}\leq\mathrm{a_{k2}}\leq\mathrm{a_{k3}}\leq\mathrm{a_{k4}}<1\). If \(\mathrm{a_{k2}}=\mathrm{a_{k3}}\), then \(\mathrm{A_{k}}\) is given by a triangular fuzzy number; if \(\mathrm{a_{k1}}=\mathrm{a_{k2}}=0\), then \(\mathrm{A_{k}}\) is given by an R-function fuzzy number; and if \(\mathrm{a_{k3}}=\mathrm{a_{k4}}=1\), \(\mathrm{A_{k}}\) is given by a L-function fuzzy number. All comments posted by users relating to a spatial entity \(\mathrm{e_{i}}\), after the analysis and cleaning of their content performed by the _text parsing_ component, are grouped to form a \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Document**} & \multicolumn{6}{c}{**Fuzzy Set**} \\ \cline{2-10}  & **Very Low** & **Low** & **Medium–Low** & **Medium** & **Medium–High** & **High** & **Very High** & **Relevance** \\ \hline Document 1 & 0.00 & 0.05 & 0.23 & **0.44** & 0.23 & 0.05 & 0.00 & Medium \\ Document 2 & 0.00 & 0.00 & 0.00 & 0.11 & **0.78** & 0.11 & High \\ Document 3 & **0.51** & 0.38 & 0.12 & 0.00 & 0.00 & 0.00 & Very Low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of assignment of the relevance of an emotional category to documents. document \(D_{i}\). After performing this process for each spatial entity, the corpus of documents D = {d\({}_{1}\), d\({}_{2}\),..., d\({}_{N}\)} will be created. Then, the _term filtering_ and the _TF-IDF_ components are executed in order to obtain the emotional category matrix, whose element R(d\({}_{i}\),c\({}_{j}\)) i = 1,..., N j = 1,..., M represents the crisp normalized relevance of the emotional category c\({}_{j}\) in the i\(th\) document d\({}_{i}\). For each document, the _document classification_ component is executed in order to obtain the fuzzy relevance of each pleasant and unpleasant emotional category in the document; these results will be added to the correspondent spatial entity. The fuzzy relevance of the _jth_ emotional category c\({}_{j}\) in the i\(th\) document d\({}_{i}\) is given by the label of the fuzzy set of the relevance fuzzy partition to whom the element R(d\({}_{i}\),c\({}_{j}\)) belongs with the highest membership degree. Thematic maps showing the spatial distribution of the fuzzy relevance of each emotional category are produced.