# Post-Authorship Attribution Using Regularized Deep Neural Network
## 4 Experimental Results
### Results
#### 4.3.1 Quantitative Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**CCAT50**} & \multicolumn{2}{c}{**IMDB62**} & \multicolumn{2}{c}{**Blogs50**} & \multicolumn{2}{c}{**Twitter50**} \\ \cline{2-9}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline Lexical and Topical [1] & & **97.72** & 97.72 & & & 85.04 & \\ PMSVM [19] & & & 93.00 & & 59.70 & 56.70 & & 70.00 \\ BertA [35] & & 90.58 & 94.10 & & 61.19 & & & \\ SCAP [45] & & 94.80 & 94.80 & 41.60 & 41.60 & 82.50 & 82.50 \\ Imposters [50] & & 49.90 & 76.90 & & 22.60 & & 52.50 \\ SVM Affix-purctuation & & 69.30 & & & & & \\ SVM with frequent & 67.00 & 70.30 & 81.40 & & & & \\ Continuous n-gram & & 94.80 & & & & & \\ (2,3,4) [60] & & & & & & 31.83 & \\ SCAP n = 5 and L = 100 [68] & & & & & & 51.05 & \\ IAF, RLP with n = 4, & & & & & & 55.50 & \\ L = 100 [68] & & & & & & 60.00 & \\ Character n-grams and & & & & & & 53.20 & \\ Word n-grams [71] & & & & & & 64.54 & 77.13 \\ Character n-grams [71] & & & & & & & \\ n-gram and POS-tag [72] & & & & & & 80.50 & 80.50 \\ IAF, RLP with n = 4, & & & & & & 64.54 & 77.13 \\ L = 1000 [74] & & & & & & 64.54 & 77.13 \\ CNN word [75] & & 84.30 & 84.30 & 65.70 & 43.00 & 80.50 & 80.50 \\ CNN character [75] & & 91.70 & 91.70 & 49.40 & 48.10 & 73.20 & **86.80** \\ n-gram CNN [76] & 76.50 & 91.21 & & 53.09 & & & \\ Syntax-tree CNN [77] & 81.00 & 96.16 & & 56.73 & & & \\ DeepStyle [85] & 80.45 & & 90.51 & & & 64.80 & \\ BEET [87] & 66.20 & & **97.90** & & 33.50 & & \\ LDAH-5 with the topic [80] & & & 72.00 & 72.00 & 18.30 & 18.30 & & 38.30 \\ \hline
**RDNN Method** & **93.20** & **92.20** & 96.50 & 95.75 & **63.40** & **76.69** & **86.48** & 84.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance accuracy and F1 score of the RDNN and SOTA methods for PAA subtask. Figure 4: Performance accuracy and loss curves over epoch on the Blogs50 datasets: (**a**) accuracy, (**b**) loss. Figure 3: Performance accuracy and loss curves over epoch on the IMDB62 dataset: (**a**) accuracy, (**b**) loss. Figure 2: Performance accuracy and loss curves over epoch on the CCAT50 dataset: (**a**) accuracy; (**b**) loss. The performance of the RDNN method was further tested to determine how author number and post length increased with fewer iterations for all the experimental datasets. Figure 6 demonstrates how the RDNN method performs in comparison to the SOTA methods on CCAT50 and IMDB62 datasets. The RDNN method has superior accuracy in three experimental datasets and achieves comparable performance in the fourth experimental dataset when compared to the other SOTA methods investigated. The results of several SOTA methods to analyze the stability of the RDNN method are reported for Blogs50 and Twitter50 datasets, as shown in Tables 5 and 6. Five independent runs were performed on Blogs50 and Twitter50 datasets. The maximum variability in accuracy is within 0.13%, to indicate that the RDNN method is still stable even if the precision is relatively low. The RDNN method can be postulated to be generally superior to the investigated SOTA methods on a large-scale dataset related to style-related tasks.