# An Enhanced Neural Word Embedding Model for Transfer Learning
## 5 Experiments
### Comparison with Previous Techniques
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extraction Methods**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Performance Score**} \\ \cline{3-6}  & & **Accuracy** & **Hamming Loss** & **Recall** & **Precision** & \(f1\)**-Score** \\ \hline \multirow{2}{*}{**Word2 Vec**} & KNN & 0.8827 & 0.2671 & 0.6792 & 0.7745 & 0.5821 \\  & XGB & 0.9454 & 0.1814 & 0.6789 & 0.8723 & **0.6545** \\  & SVM & 0.7677 & 0.2318 & 0.7865 & 0.8124 & 0.6728 \\  & RF & 0.7212 & 0.2436 & 0.5267 & 0.9013 & 0.5876 \\  & LR & 0.8012 & 0.2764 & 0.6014 & 0.7067 & 0.5312 \\ \hline \multirow{2}{*}{**FastText**} & KNN & 0.9224 & 0.2242 & 0.7174 & 0.9112 & 0.6012 \\  & XGB & 0.9745 & 0.2214 & 0.6489 & 0.9124 & **0.7322** \\  & SVM & 0.7436 & 0.2518 & 0.8913 & 0.8618 & 0.6523 \\  & RF & 0.7345 & 0.2142 & 0.5398 & 0.9533 & 0.6055 \\  & LR & 0.8356 & 0.2551 & 0.6295 & 0.7245 & 0.5423 \\  & LSTM & **0.9814** & **0.1801** & 0.7121 & 0.8942 & 0.7735 \\  & CNN & 0.9298 & 0.1954 & 0.9012 & 0.7823 & 0.5945 \\ \hline \multirow{2}{*}{**BanFastText** **(CBOW)**} & KNN & 0.9321 & 0.2507 & 0.7401 & 0.9421 & 0.6217 \\  & XGB & 0.9645 & 0.0274 & 0.6267 & 0.9024 & **0.7245** \\  & SVM & 0.7581 & 0.2318 & 0.9003 & 0.8918 & 0.6728 \\  & RF & 0.7157 & 0.2542 & 0.5073 & 0.9333 & 0.5855 \\  & LR & 0.8824 & 0.2031 & 0.6419 & 0.7418 & 0.5946 \\  & LSTM & **0.9814** & **0.1801** & 0.7121 & 0.8942 & 0.7812 \\  & CNN & 0.9425 & 0.1759 & 0.9412 & 0.8019 & 0.6121 \\ \hline \multirow{2}{*}{**BanFastText** **(Skip-gram)**} & KNN & 0.9664 & 0.1131 & 0.6309 & 0.9060 & 0.6733 \\  & XGB & 0.9723 & **0.1105** & 0.6309 & 0.9298 & 0.7517 \\  & SVM & 0.7687 & 0.2112 & 0.7012 & 0.9666 & 0.7214 \\  & RF & 0.7245 & 0.2345 & 0.5623 & 0.8733 & 0.5564 \\  & LR & 0.8766 & 0.1233 & 0.7421 & 0.7235 & 0.5823 \\  & LSTM & **0.9896** & 0.1802 & 0.7462 & 0.8344 & **0.7723** \\  & CNN & 0.9623 & 0.1968 & 0.9504 & 0.8272 & 0.6292 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of different feature extractors in the text classification of the BanFakeNews dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extraction Methods**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**Performance Score**} \\ \cline{3-7}  & & **Accuracy** & **Hamming Loss** & **Recall** & **Precision** & \(f\)**1-Score** \\ \hline
**Word2 Vec** & KNN & 0.8103 & 0.1897 & 0.5166 & 0.7277 & 0.6042 \\  & XGB & **0.8586** & **0.1414** & 0.6178 & 0.8346 & 0.7100 \\  & SVM & 0.8395 & 0.1605 & 0.5649 & 0.8043 & 0.6637 \\  & RF & 0.8005 & 0.1994 & 0.2915 & 0.9897 & 0.4504 \\  & LR & 0.8395 & 0.1604 & 0.8036 & 0.6811 & **0.7373** \\ \hline
**FastText** & KNN & 0.8848 & 0.1151 & 0.6873 & 0.875 & 0.7699 \\  & XGB & **0.9225** & **0.0774** & 0.8383 & 0.8795 & 0.8584 \\  & SVM & 0.9209 & 0.0660 & 0.8777 & 0.8776 & 0.8856 \\  & RF & 0.8796 & 0.1113 & 0.6933 & 0.9310 & 0.7948 \\  & LR & 0.8608 & 0.1312 & 0.8701 & 0.7245 & 0.7906 \\  & LSTM & 0.9089 & 0.0808 & 0.8432 & 0.8942 & **0.8901** \\  & CNN & 0.7138 & 0.2891 & 0.5103 & 0.7349 & 0.5927 \\ \hline
**BanFastText** & KNN & 0.9001 & 0.0999 & 0.7401 & 0.8844 & 0.8059 \\
**(CBOW)** & XGB & 0.9292 & 0.0707 & 0.8625 & 0.8825 & 0.8724 \\  & SVM & **0.9441** & **0.0508** & 0.9003 & 0.9003 & 0.9003 \\  & RF & 0.9098 & 0.0901 & 0.7673 & 0.8959 & 0.8266 \\  & LR & 0.8793 & 0.1206 & 0.6419 & 0.8985 & 0.7488 \\  & LSTM & 0.9221 & 0.0752 & 0.8511 & 0.9012 & **0.9101** \\  & CNN & 0.7238 & 0.2091 & 0.5203 & 0.7049 & 0.6027 \\ \hline
**BanFastText** & KNN & 0.8927 & 0.1072 & 0.6937 & 0.8796 & 0.7831 \\
**(Skip-gram)** & XGB & 0.9229 & 0.0773 & 0.8429 & 0.8773 & 0.8697 \\  & SVM & **0.9373** & **0.0526** & 0.8836 & 0.8917 & 0.8978 \\  & RF & 0.8988 & 0.1003 & 0.6752 & 0.9033 & 0.8126 \\  & LR & 0.8823 & 0.1287 & 0.6042 & 0.9049 & 0.8246 \\  & LSTM & 0.9123 & 0.0701 & 0.8712 & 0.9101 & **0.9134** \\  & CNN & 0.7308 & 0.2104 & 0.5813 & 0.7191 & 0.6107 \\ \hline \hline \end{tabular}
\end{table}