## 3 Fundamental Concepts
### Data Mining from Twitter
The first one refers to the lack of localization in a large subset of the considered input data. To handle this issue, _tweets_ without GPS tags are removed [56; 59] or have their location estimated using the user profile information, _tweet_ history and entities mentioned in the _tweet_ message [60]. The second issue is related to the frequent inclusion of large amounts of informal and irregular sentences and grammatical errors presented in \(w.D\). This unstructured text may leads to poor text analysis that affects the accuracy of analytical algorithms [61]. However, there are some techniques to handle this problem [50; 54; 62]. The final step of the mining process is to design effective analytical methods from the processed data [10]. Classification and prediction are two forms of data analysis that can be used to extract models describing important data classes or predict future trends [53]. In classification problems, an unlabeled entry \(w\) is classified into a category by using classification rules extracted from a previously analyzed dataset of objects with the same nature as \(w\). For example, a _tweet_ can be classified as _positive_ or _negative_, indicating if it has positive or negative emotions [63]. Prediction problems, on the other side, aim to perform a numerical prediction related to some event (e.g., estimate how much a client will spent in an online store). In _Twitter_, studies including both prediction and classification problems were performed. In general, both approaches are combined to achieve a major goal such as real-time event detection [55; 56; 59] or opinion mining [62; 63]. Independent of the problem nature (prediction or classification), methods for analytical processing inevitably involve machine or statistical learning concepts [45; 47; 63]. Moreover, learning tasks are typically divided into _supervised_ and _unsupervised_ learning approaches. While _supervised_ learning requires tries to predicts the results within a continuous output, _unsupervised_ learning produces an output by clustering the data based on relationships among its variables [47]. Usually, supervised approaches are applied for detecting specified behaviors [35; 39], while unsupervised approaches are employed in unspecified behaviors [56; 59]. However, hybrid approaches can be employed for detecting on-line events which responds to a pre-defined criteria [55]. For the proposed approach, the input _Twitter_ data flow is preprocessed before being considered for event detection and classification. In an initial step, the text, geotags, timestamp and id information of each tweet \(w\) is maintained while the other features are removed, resulting in the vector of features \(w.f=[w.id,w.D,w.t,w.A]\) previously mentioned. Before entering the event detector, we extract entities and noun phrases of each \(w.D\) using a NLP tool specific for tweets [64] and use them as keywords on the event detection process, which is an analytical unsupervised process discussed on Section 4.2.1. On the other hand, we employ the Multinomial Naive Bayes (NB) supervised analytical procedure on the event grader to classify \(w.D\) and obtain its scope value. To tune NB and obtain better results, we apply three common NLP strategies [48; 50]: tokenization, stemming and stopword removal. The procedures for scope calculation are detailed in Section 4.2.2.