## 1 Introduction
To reach a trade-off between the computational cost and the accuracy of the classifications in [11], an emotion detection method was proposed to classify social documents according to the prevailing emotional category in which a lightweight NLP approach was used for the detection and annotation in the text of terms belonging to specific emotional categories. An extension of the Fuzzy C-means (FCM) partitive clustering algorithm is applied to classify documents where the term frequency inverse document frequency measure (TF-IDF) is used to assess the relevance of emotion categories in the document. A variation of this framework is used in [12] to classify document extracts from tweets; the authors applied an entropy variation of FCM to classify documents using the primary and secondary pleasant and unpleasant Plutchik emotion categories and they showed that their approach improves the classification accuracy of the emotion classification framework [11]. In [13], a new emotion classification framework called FREDoC is proposed; it is based on a fuzzy-based emotion classification model applied instead of the FCM clustering algorithm to classify documents considering the primary and secondary Plutchik emotion categories. This approach overcomes the critical issues of the frameworks proposed in [11; 12], with it not requiring a mapping process between the final cluster and the emotional categories and providing an immediate linguistic interpretation of the final classification of the documents. In [13], the authors used a multi-classification technique to assign the document to the emotional categories with relevance greater than a preset threshold. The authors showed that this approach provides a better emotional classification with respect to [11; 12], also assigning to the document significant and not negligible emotions, even if non-predominant. An important use of the lightweight document emotion classification approach concerns the assessment of the prevailing emotions of citizens and tourists about the places and events experienced as revealed on a social network. Each document is associated with a particular place in the area of study and with a specific time interval. The results of the classification are thematic maps in which the spatial distribution of the prevailing emotional categories and their evolution over time are highlighted. The framework [11] is implemented in a geographical information system (GIS) in [14] for classifying urban districts based on the feelings of citizens detected from social streams. In [15], a GIS-based emotion classification framework is proposed in which the emotion document classification model [11] is used to classify the subzones in which the area of study is partitioned based on the relevance of pleasant and unpleasant emotion categories. In this study, we provide a technique for capturing the prevailing feelings of users about service facilities located within the area of study expressed through reviews, opinions, and comments posted on the web. In order to carry out multi-classification of the prevailing emotions, the FREDoC model is executed to measure the relevance of each emotional category. We implement this method on a GIS-based platform where the analyzed infrastructures on which the classification is made are located on the map. The proposed method has the advantage of providing, with short computation times, a multi-classification of the prevailing emotional categories assigned by users to each service facility, allowing the spatial distribution of its relevance to be constructed for each emotional category and to evaluate where they are located in the service facilities in which pleasant and unpleasant emotions prevail. Another goal of our research is to improve the assessments made by approval scales produced by review-collecting providers which are based on the subjective scores entered by users on a scale of 1 to 5.