## 5 Analysis and Verification of Text Events Categorizer Approach--Classification Results
### Classification Quality Measures and Selected Datasets  
In order to assess the quality of the classification, the three most popular quality measures were used:  
* Measure of effectiveness (_accuracy_): \[A=\frac{TP+TN}{TP+TN+FP+FN}\] (26)
* Measure of precision (_precision_): \[P=\frac{TP}{TP+FP}\] (27)
* Sensitivity measure (_recall_): \[R=\frac{TP}{TP+FN}\] (28) The following notations were adopted in the above formulas:
* _TP_ (_True Positive_) is the number of texts correctly classified as a given category;
* _TN_ (_True Negative_) is the number of texts correctly classified as other than the examined category;
* _FP_ (_False Positive_) is the number of texts incorrectly classified as the examined category;
* _FN_ (_False Negative_) is the number of texts incorrectly classified as other than the examined category.  
In order to conduct the classification tests by using the _Text Events Categorizer_ system, it was decided to select three sets of text data, which were given the following designations along with names referring to the companies they come from:  
* set _Z\({}_{1}\)--Experian Informatic Solutions_;
* set _Z\({}_{2}\)--Bank Of America_;
* set _Z\({}_{3}\)--Reuters Articles_.  
The first two datasets _Experian Informatic Solutions_ and _Bank Of America_ come from the _Consumer Complaint Database_[23] and are closely related to the issues of work discussed in Section 3. They contain reports from customers of the two above-mentioned American companies, which were sent to customer service offices. The sets _Z\({}_{1}\)_ and _Z\({}_{2}\)_ are therefore sets of text events, perfectly fitting into the problem described in Section 2.  
The last _Reuters Articles_ dataset consists of news articles from the _Reuters_ news agency [24]. The set _Z\({}_{3}\)_ is a completely different set in relation to the other two sets. Press articles are characterized by a completely different language and structure than reports received by customer service offices. The _Z\({}_{3}\)_ set was chosen precisely because of its difference, which will allow us to verify the quality of classification for text data of a completely different type, coming from a market sector very distant from the activities of the companies from which the data constituting the _Z\({}_{1}\)_ and _Z\({}_{2}\)_ sets come from.  
Set _Z\({}_{1}\)--Experian Informatic Solutions_  
The _Z\({}_{1}\)_ dataset contains 4274 texts divided into three categories. Due to the long names of individual labels (category names), it was decided to introduce the following designations to represent each of them:  
* Category _K\({}_{1}\)--credit-reporting company's investigation_;* Category \(K_{2}\)--_incorrect information on credit report_;
* Category \(K_{3}\)--_problem with a credit-reporting company's investigation into an existing problem_.  
The number of texts for each of the three labels is listed in Table 1.  
Set \(Z_{2}\)--_Bank Of America_  
The \(Z_{2}\) dataset consists of 4318 texts divided into three categories. The structure of the _Bank Of America_ dataset is presented in Table 2.  
Set \(Z_{3}\)--_Reuters Articles_  
The largest set of texts is the \(Z_{3}\) set, which includes 6290 press articles divided into four groups (Table 3).