## 5 Analysis and Verification of Text Events Categorizer Approach--Classification Results
### Discussion
#### 5.4.1 Classification Efficiency  
Classification efficiency is by far the most important and also the most intuitive measure of classification quality. It informs us about the percentage of correctly classified texts.  
The classification efficiency for all the experiments was above 70%, which is definitely a satisfactory result (Table 4 and Figure 7). The most difficult set of texts for classification turned out to be \(Z_{1}\), for which the worst classification efficiency was achieved, both in _Series 1_ and _Series 2_ (values slightly exceeding 70%). The middle place in the hierarchy of classification efficiency with a much better result (about 80% depending on the series) is occupied by the \(Z_{2}\) set. The best efficiency values, significantly exceeding 90% in both test series, were obtained for the \(Z_{3}\) set.  
_Structure of the Set and the Efficiency of Classification_  
Based on the analysis of the obtained results, it can be seen how the structure of the set affects the effectiveness of classification. For this purpose, it is necessary to look at the names of the categories into which the texts from individual collections were classified.  
All three labels for \(Z_{1}\) are very similar, each containing the expression _credit report_. After reading these three names, we may come to the conclusion that it is practically impossible for a person without proper banking knowledge and knowledge of _Experian Informatic Solutions_ procedures and methods to correctly classify the texts in this collection on their own. Such a slight difference in meaning between the names of the categories translates into a very high syntactic similarity between the texts from different labels in this collection, thus making it very difficult to classify. Nevertheless, the _Text Events Categorizer_ system coped quite well with the classification task set before it, achieving a high efficiency of over 70% (the analysis of the calculated precision and sensitivity values in the following subsections allows for a better interpretation of the obtained efficiency).  
In the case of the \(Z_{2}\) set, the names of the categories are much more diverse. Despite the fact that they still all concern the banking sector, each of them represents a different part of it. Such a structure of the set implies less syntactic similarity between the texts representing different labels, which is the reason for the higher classification efficiency than in the case of \(Z_{1}\).  
The \(Z_{3}\) set is completely different from its predecessors. The text data in this collection are press releases, divided into thematic categories. In this case, the texts from individual categories are very different from each other, both in meaning and syntactically. Therefore, _Reuters Articles_ are by far the easiest to classify and achieved the best performance results.  
_Performance in Series 1 vs. Performance in Series 2_  
The Figure 8 clearly shows that each of the experiments in _Series 2_ yielded a higher classification efficiency value than the corresponding experiment in _Series 1_. The difference between the calculated values ranges from 1.2% for the \(Z_{1}\) set to less than 6% for the \(Z_{2}\) set. For each set, the difference in the classification efficiency for the _Series 1_ and _Series 2_ experiments is therefore noticeable.  
After considering the research results listed above for both series, a preliminary thesis should be put forward that the use of the proposed novel mechanisms in the extraction process, the _dynamic extension of stop list_ and _weighted keywords_, has a positive impact on the efficiency of the classification for each of the analyzed datasets. However, the effectiveness is only one of the components used to assess the quality of the classification.