# with the same dimensionality as all the other vectors
## 5 Comparison with Results of Studies Available in the Literature  
Exactly the same results are not available in the literature for all the methods addressed in this paper. Therefore, the comparison made was focused on the works described in [16, 17, 18].  
The obtained results of classification accuracy are consistent with those available in the literature. The effectiveness of the methods tested in [16] ranges from 0.8 to 0.95. A comparison of the obtained effectiveness for the analysis of news texts from the [16] and our work is given in Table 6.  
As can be seen, the efficiency value is practically the same. The number of epochs needed to train the CBOW method is also similar. In the case of the Skip-gram method, the number of epochs is even higher than the research conducted in this paper. This is an additional confirmation that the Skip-gram method, due to the creation of complex vectors, needs more time to achieve satisfactory performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Effectiveness [16]** & **Epoch [16]** & **Effectiveness** & **Epoch** \\ \hline CBOW & 0.9341 & 137 & 0.941 & 144 \\ Skip-gram & 0.9147 & 197 & 0.926 & 152 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of classification performance results for CBOW and Skip-gram methods with the work [16].  
In addition, the authors of [16] have included graphs showing the variation of accuracy depending on the epoch. These results are similar to those obtained in our work, except that in [16], as early as around epoch 50, the effectiveness is close to the final effectiveness, while in the case of our work, only for epoch 130, the results were close. This behavior may be due to the choice of the test set for the methods. The more difficult it is, the more time will be needed to train the same effective method.  
In contrast, the results in [17] are different. The summary includes a comparison of a class of texts represented by many documents and by few documents. A comparison of these data with the present work is shown in Table 7.  
The largest discrepancy is seen here for the doc2 vec method. The authors of [17] point out that the behavior of this method in the tests they performed is different from those available in [13]. This result may be influenced by the authors' use of Chinese language texts in the context of public financial documents for classification.  
The same methods in many publications have been tested on different datasets, and it is the selection of the dataset that determines their effectiveness. For comparison, in [19], CBOW was compared for several datasets and the respective effectiveness was 61.8% for MR [19], 77.9% for SST [20], and 87.1% for IMDB [21]. For the same datasets, tests were also conducted for the Skip-gram method; the corresponding MR was 63.5%, SST was 77.3%, and IMDB was 89.1%. Which clearly allows us to conclude that it is the complexity of the data to be created and then classified that determines the effectiveness of the method. Hence, different methods for the same datasets have similar effectiveness in terms of values. It is also worth noting that the effectiveness of the Skip-gram method within the study [18] is higher than CBOW, contrary to what came out in this study.