## 5 Results
### Results Obtained on Literary Texts
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Number of**} & \multicolumn{6}{c}{**Accuracy of Models, \%**} \\ \cline{2-9}  & **SVM** & **LR** & **NB** & **DT** & **RF** & **KNN** \\ \hline
2 & 92.4 \(\pm\) 1.1 & **93.2 \(\pm\) 2.3** & 85.7 \(\pm\) 2.7 & 90.1 \(\pm\) 1.1 & 92.5 \(\pm\) 2.2 & 86.9 \(\pm\) 3.3 \\
5 & 84.5 \(\pm\) 3.4 & 82.2 \(\pm\) 4.3 & 72.7 \(\pm\) 4.8 & 81.1 \(\pm\) 3.2 & **88.5 \(\pm\) 3.1** & 78.0 \(\pm\) 2.6 \\
10 & 72.2 \(\pm\) 5.6 & 68.7 \(\pm\) 5.5 & 59.4 \(\pm\) 4.9 & **75.3 \(\pm\) 2.2** & 70.6 \(\pm\) 1.1 & 71.4 \(\pm\) 4.9 \\
20 & 55.2 \(\pm\) 4.2 & 52.3\(\pm\) 4.8 & 49.3 \(\pm\) 5.3 & 33.6 \(\pm\) 3.1 & **59.0 \(\pm\) 3.4** & 57.4 \(\pm\) 2.6 \\
50 & 33.2 \(\pm\) 4.8 & 27.7 \(\pm\) 3.3 & 22.8 \(\pm\) 4.1 & 16.1 \(\pm\) 5.1 & 31.4 \(\pm\) 4.1 & **40.2 \(\pm\) 3.4** \\ Avg. & 67.5 \(\pm\) 3.9 & 64.8 \(\pm\) 4.1 & 57.9 \(\pm\) 4.6 & 59.3 \(\pm\) 2.9 & **68.4 \(\pm\) 2.9** & 66.8 \(\pm\) 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of author identification using ML trained on the TF-IDF. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Number of**} & \multicolumn{6}{c}{**Accuracy of Models, \%**} \\ \cline{2-9}  & **SVM** & **LR** & **NB** & **DT** & **RF** & **KNN** \\ \hline
2 & 92.4 \(\pm\) 1.1 & **93.2 \(\pm\) 2.3** & 85.7 \(\pm\) 2.7 & 90.1 \(\pm\) 1.1 & 92.5 \(\pm\) 2.2 & 86.9 \(\pm\) 3.3 \\
5 & 84.5 \(\pm\) 3.4 & 82.2 \(\pm\) 4.3 & 72.7 \(\pm\) 4.8 & 81.1 \(\pm\) 3.2 & **88.5 \(\pm\) 3.1** & 78.0 \(\pm\) 2.6 \\
10 & 72.2 \(\pm\) 5.6 & 68.7 \(\pm\) 5.5 & 59.4 \(\pm\) 4.9 & **75.3 \(\pm\) 2.2** & 70.6 \(\pm\) 1.1 & 71.4 \(\pm\) 4.9 \\
20 & 55.2 \(\pm\) 4.2 & 52.3\(\pm\) 4.8 & 49.3 \(\pm\) 5.3 & 33.6 \(\pm\) 3.1 & **59.0 \(\pm\) 3.4** & 57.4 \(\pm\) 2.6 \\
50 & 33.2 \(\pm\) 4.8 & 27.7 \(\pm\) 3.3 & 22.8 \(\pm\) 4.1 & 16.1 \(\pm\) 5.1 & 31.4 \(\pm\) 4.1 & **40.2 \(\pm\) 3.4** \\ Avg. & 67.5 \(\pm\) 3.9 & 64.8 \(\pm\) 4.1 & 57.9 \(\pm\) 4.6 & 59.3 \(\pm\) 2.9 & **68.4 \(\pm\) 2.9** & 66.8 \(\pm\) 3.5 \\ accuracy & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of author identification using NNs. In contrast to the classification of literary texts, the results obtained allow us to conclude that the classical ML methods are ineffective using the formed feature space. This is because the comments are short. The content of the comments reflects the emotions of the author about a commented post, so short statements and sentences prevail in the dataset. Due to this, the text volume is too small to obtain individual characteristics of the author even in a carefully formed feature space. SVM with experimentally chosen features trained on feature space achieves a maximum accuracy of 72% for two authors, while deep NNs are able to classify with an accuracy of 96% for the same task. This fact is explained by the ability of deep NNs to select implicit informative features automatically. The use of TF-IDF instead of feature vector gave an advantage only for two models (LR and NB). The accuracy obtained of all models in the cases of 20 and 50 authors is significantly inferior to the results obtained for literary texts. FastText outperforms LSTM+CNN and BERT models in accuracy for all sets of authors considered and learns 39% faster on average. In addition, for 2 and 10 authors, the accuracy of fastText is higher than BiLSTM, and in the case of 50 authors, fastText outperforms all other models. FastText outperforms all deep neural networks considered by the learning rate by 42% on average.