# A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams
## 1 Introduction  
Detection and classification of emotions from social streams are one of the main goals of Sentiment Analysis (SA). Generally, Deep Learning algorithms are executed to detect emotions from massive data [1]. However, in cases where the size of the available labeled data is small, it is difficult and expensive to construct large training sets; using semi-supervised and unsupervised algorithms that also employ unlabeled data is an effective option [2]. Semi-supervised learning algorithms [3] use large unlabeled data in combination with the available labeled data in various learning processes. They can improve classification performances using unlabeled data to extract relevant information necessary for classifying data points [4]. Unsupervised learning methods are applied for mining data when no annotation is available; various clustering algorithms were used to detect embedded knowledge in massive data streams [5]. In [6], a lightweight model based on an extension of the Fuzzy C-means algorithm (FCM) [7; 8], called Extended Fuzzy C-Means (EFCM) [9], is applied to social streams for emotion-based classification. Using this procedure, documents are classified into emotional categories by being assigned to the emotional category that corresponds to the fuzzy cluster to which it has the highest membership degree. The authors showed that this approach produces good classification accuracy. This model is implemented in a Geographical Information System (GIS) in [10] to classify urban districts based on the orientation of citizens detected from social streams. The document classification model [6] represents a significant trade-off between classification accuracy and computational complexity. However, it has two main critical points:  
* The number of clusters must match the number of emotional categories, and there can be no a priori one-to-one mapping between clusters and emotional categories;
* The centers of the initial clusters are set randomly. This produces an average increase in execution times and allows the algorithm to converge toward a local minimum;
* the document is assigned to a single emotional category, not considering emotional categories corresponding to clusters to which the document belongs with a non-negligible membership degree. A new framework for emotion-based classification from social streams, using an entropy-based weighted variant of FCM, named EwFCM, to classify the data gathered from streams, is provided to overcome the cluster center's initialization problem in [11]. EwFCM applies a fuzzy entropy measure to optimize the initialization of the cluster centers. Test results applied on Twitter datasets show that this emotion classification framework provides better results than the framework proposed in [10] in terms of classification accuracy and execution time. However, this approach does not solve the problems connected to the constraint of fixing the number of clusters equal to the number of emotional categories and to the choice of neglecting the clusters to which the document belongs with a secondary but not negligible membership degree. To overcome these drawbacks, a new fuzzy emotion-based classification method, called FREDoC (Fuzzy Relevance Emotions Document Classification), is proposed in [12] in which, instead of executing a fuzzy clustering approach to classify a document by assigning it to the prevailing emotional category, an emotion relevance index is employed to determine if an emotional category in a text is relevant, and it is based on the well-known TF-IDF metric. Fuzzy linguistic labels are used to explain the emotional relevance, defined by constructing a specific fuzzy partition; the documents are classified considering those emotional categories whose relevance in the document is not negligible. In [13], the FREDoC emotion-based multiclassification method is implemented in a GIS framework to classify urban areas by the more relevant critical issues highlighted by citizens.