# with the same dimensionality as all the other vectors
## 4 Results of Experiments and Their Analysis  
The realized experiments contained 10 tests. Each of them used one of five implemented text vectorization methods in combination with one of the two text classification methods. This yielded the following test cases:  
* vectorization by the CBOW method with word vector averaging for texts, classification by the k-nearest neighbors method, denoted in the results as CBOW k-NN,
* Skip-gram vectorization with word vector averaging for texts, classification by the k-nearest neighbors method, denoted in the results as Skip-gram k-NN,
* CBOW vectorization with the tf-idf method, k-nearest neighbors classification, denoted in the results as CBOW tf-idf k-NN,
* DM-PV vectorization, k-nearest neighbor classification, doc2 vec k-NN,
* CBOW vectorization with word vector averaging for texts, Naive Bayesian Classifier classification, denoted as CBOW NBC in the results,
* Skip-gram vectorization with word vector averaging for texts, Naive Bayesian Classifier classification, denoted as Skip-gram NBC in the results,
* CBOW vectorization with tf-idf, Naive Bayesian Classifier, denoted as CBOW tf-idf NBC in the results,
* Skip-gram vectorization using the tf-idf method, Naive Bayesian Classifier, denoted in the results as Skip-gram tf-idf NBC,  
As can be seen from the data obtained, none of the methods stood out in terms of efficiency in the case studied. For the k-nearest neighbor method, the final results are in the range of [0.92; 0.95], while for the Naive Bayesian Classifier, they are in the range of [0.94; 0.96]. The results obtained using NBC show much smaller differences than the k-NN method. At the same time, it should be noted that the classification performance of this method is better than the classification via the much simpler k-NN method. Regardless of the classification method chosen, the Continuous Bag of Words method with feature extraction using Term Frequency--Inverse Document Frequency performed best. In the case of classification with the k-NN method, an accuracy score of 0.945 was obtained, while using the Naive Bayesian Classifier, it was 0.955. However, for the studied case of document topic classification, the lowest performance results were obtained using the Skip-gram method when averaging the word vectors. The \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accuracy** & **Epoch** \\ \hline CBOW NBC & 0.951 & 153 \\ Skip-gram NBC & 0.947 & 157 \\ CBOW tf-idf NBC & 0.955 & 146 \\ Skip-gram tf-idf k-NN & 0.949 & 179 \\ Dov2 vec k-NN & 0.950 & 125 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of Naive Bayesian Classifier classification for different vectorization methods. Figure 5: Accuracy of Naive Bayesian Classifier classification for selected text vectorization methods by epoch. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CBOW** & **SkipGram** & **CBOW TF-TDF** & **SkipGram TF-TDF** & **Doc2 vec** \\ \hline Business precision & 0.95 & 0.98 & 0.9 & 0.97 & 0.91 \\ Business recall & 0.97 & 0.91 & 0.9 & 0.9 & 0.9 \\ Business F1 & 0.96 & 0.94 & 0.9 & 0.94 & 0.91 \\ Entertainment precision & 0.99 & 0.91 & 0.93 & 0.93 & 0.95 \\ Entertainment recall & 0.97 & 0.97 & 0.95 & 0.96 & 0.99 \\ Entertainment F1 & 0.98 & 0.94 & 0.94 & 0.95 & 0.97 \\ Politics precision & 0.93 & 0.96 & 0.99 & 0.93 & 0.97 \\ Politics recall & 0.84 & 0.97 & 0.97 & 0.96 & 0.96 \\ Politics F1 & 0.89 & 0.96 & 0.98 & 0.94 & 0.97 \\ Sport precision & 0.97 & 0.94 & 0.97 & 0.97 & 0.96 \\ Sport recall & 0.99 & 0.95 & 0.98 & 0.96 & 0.96 \\ Sport F1 & 0.98 & 0.95 & 0.97 & 0.96 & 0.96 \\ Tech precision & 0.93 & 0.94 & 0.98 & 0.95 & 0.95 \\ Tech recall & 0.99 & 0.95 & 0.98 & 0.97 & 0.95 \\ Tech F1 & 0.96 & 0.94 & 0.98 & 0.96 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Precision, recall, and F1 scores for all vectorization methods with Naive Bayesian Classifier classification by specific topics. classification of previously vectorized text with this method using k-NN yielded a score of 0.926 while using NBC yielded a score of 0.947.