## 5 Analysis and Verification of Text Events Categorizer Approach--Classification Results
### Discussion
#### 5.4.2 Accuracy and Sensitivity of Classification  
The effectiveness of the classification is often not sufficient to objectively assess the quality of the classification carried out. To obtain a fuller picture of how effective the individual experiments were, we need to use other measures of classification quality--accuracy and sensitivity. High Efficiency and Low Classification Sensitivity  
This part focuses on the values of the accuracy and sensitivity measures for the \(Z_{1}\) dataset (Tables 5 and 6 and Figures 8 and 9), and we compare them with a relatively high and satisfactory classification efficiency: 71.59% in _Series 1_ and 72.79% in _Series 2_. When we first look at the classification accuracy and sensitivity diagrams for the _Experian Informatic Solutions_ dataset, we can easily see that one light gray bar is missing in both diagrams. It turns out that both the accuracy and sensitivity for the \(K_{3}\) category for the _Series 1_ classification have a value of zero. Such values of accuracy and sensitivity measures mean that no text was classified in this category (neither correctly nor incorrectly), which shows that a high classification efficiency does not necessarily imply high quality. For the discussed experiment, the high efficiency of classification is mainly related to the large number of correct classifications of texts into the \(K_{2}\) category, which is by far the largest group of texts in this set. At this point, however, it should be noted that both the results of the sensitivity measure and measure of accuracy for the analogous experiment for the \(Z_{1}\) set in _Series 2_ is not zero, and in the discussed diagrams there is a set of dark gray bars unlike their brighter neighbors. It turns out that in the second iteration of the research, the _Text Events Categorizer_ system managed to correctly classify a small part of the texts in the \(K_{3}\) category. Such results again highlight the proposed solutions used in the _Series 2_ experiments as having a positive impact not only on efficiency but also on the overall quality of the classification. Comparison of Accuracy and Sensitivity in Both Series of Experiments  
However, to confirm the beneficial effect of using the _dynamic extension of stop list_ and _weighted keywords_ on the quality of the classification, it is necessary to compare the values achieved by the accuracy and sensitivity measures for each pair of experiments, conducted for all three examined sets. The necessary data can be found in Tables 5 and 6 and in Figures 8 and 9. After carefully analyzing this data, it should be noted that  
* The accuracy metric values are greater for the _Series 2_ experiments in most cases. The differences range from almost 10% for the _Morstgage_ category in the \(Z_{2}\) set in favor of the _Series 2_ study to less than 2% for the _Credit Card_ category and also _set \(Z_{2}\)_ in favor of the _Series 1_ experiment. Only in the case of two categories was a higher measure of accuracy obtained for the _Series 1_ experiments--in addition to the already-mentioned _Credit Card_ category for the \(Z_{2}\) set, this was also the case for the _money supply_ for the \(Z_{3}\) set. * The values of the sensitivity measure are higher for the studies from _Series 1_ in all cases, except for the \(K_{2}\) category for the \(Z_{1}\) set. The record difference in favor of the experiment from the second iteration was obtained for the _Credit Card_ label from the \(Z_{2}\) set--less than 23%. Taking into account the discussed results and summarizing the discussion of the presented research findings, a final conclusion can be drawn, confirming the noticeable and positive impact of using the proposed innovations in the feature-extraction process on the quality of the classification for all the selected datasets.