# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Product Group**} & \multirow{2}{*}{**Predicted**} & \multicolumn{3}{c}{**Actual**} \\ \cline{4-6}  & & & **Positive** & **Neutral** & **Negative** \\ \hline Logistic & Skin Make-Up & Positive & 8801 (98.6\%) & 13 (0.2\%) & 111 (1.2\%) \\ Regression & & Neutral & 296 (70.8\%) & 63 (15\%) & 59 (14.2\%) \\  & & Negative & 284 (18\%) & 10 (0.7\%) & 1279 (81.3\%) \\  & Eye Make-Up & Positive & 12,549 (98.4\%) & 17 (0.2\%) & 179 (1.4\%) \\  & & Neutral & 310 (61.9\%) & 99 (19.8\%) & 92 (18.3\%) \\  & & Negative & 298 (12.3\%) & 21 (0.8\%) & 2105 (86.9\%) \\  & Lip Make-Up & Positive & 10,202 (98.9\%) & 3 (0.0\%) & 117 (1.1\%) \\  & & Neutral & 333 (69.7\%) & 47 (9.8\%) & 98 (20.5\%) \\  & & Negative & 349 (20\%) & 17 (1\%) & 1380 (79\%) \\  & Skin Care & Positive & 50,615 (98.3\%) & 160 (0.3\%) & 720 (1.4\%) \\  & & Neutral & 1412 (52.9\%) & 850 (31.8\%) & 407 (15.3\%) \\  & & Negative & 1673 (19.8\%) & 47 (0.6\%) & 6732 (79.6\%) \\ Hair Care & Positive & 26,434 (98.8\%) & 47 (0.2\%) & 279 (1\%) \\  & Neutral & 651 (53\%) & 427 (34.7\%) & 151 (12.3\%) \\  & Negative & 581 (51.6\%) & 52 (1.4\%) & 3079 (83\%) \\ Body Care & Positive & 31,864 (99.2\%) & 52 (0.2\%) & 207 (0.6\%) \\  & Neutral & 720 (59.9\%) & 340 (28.3\%) & 143 (11.8\%) \\  & Negative & 527 (18\%) & 52 (1.8\%) & 2347 (80.2\%) \\ Perfume & Positive & 4285 (98.5\%) & 1 (0.00\%) & 63 (1.5\%) \\  & Neutral & 105 (70\%) & 13 (8.7\%) & 32 (21.3\%) \\  & Negative & 147 (17.5\%) & 4 (0.5\%) & 690 (82\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: LR confusion matrix results for each product group. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Product Group** & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F-1 Score**} \\ \hline \hline \multirow{3}{*}{Skin Make-Up} & **Highest** & **Lowest** & **Highest** & **Lowest** & **Highest** & **Lowest** & **Highest** & **Lowest** \\ \hline \multirow{3}{*}{Skin Care} & SVM & K-NN & SVM & K-NN & SVM, LR & K-NN & SVM, LR & K-NN \\  & (0.93) & (0.83) & (0.93) & (0.83) & (0.93) & (0.83) & (0.92) & (0.82) \\ \multirow{3}{*}{Eye Make-Up} & SVM, LR & K-NN & SVM, LR & K-NN & SVM, LR & K-NN & SVM, RF, & K-NN \\  & (0.94) & (0.86) & (0.94) & (0.85) & (0.94) & (0.88) & LR (0.93) & (0.84) \\ \multirow{3}{*}{Lip Make-Up} & SVM, LR & K-NN & SVM, RF, & K-NN & SVM, LR & K-NN & SVM, RF, & K-NN \\  & (0.93) & (0.85) & LR (0.92) & (0.83) & (0.94) & (0.85) & LR (0.91) & (0.83) \\ \multirow{3}{*}{Skin Care} & SVM, LR & K-NN & SVM & K-NN & SVM, LR & K-NN & SVM & K-NN \\  & (0.94) & (0.87) & (0.95) & (0.84) & (0.94) & (0.87) & (0.94) & (0.85) \\ \multirow{3}{*}{Hair Care} & SVM & K-NN & SVM & K-NN, LR & SVM & K-NN & SVM & K-NN, LR \\  & (0.95) & (0.72) & (0.95) & (0.91) & (0.95) & (0.72) & (0.94) & (0.79) \\ \multirow{3}{*}{Body Care} & SVM, RF & K-NN & SVM, LR & K-NN, DT & SVM, RF, & K-NN & SVM, LR & K-NN \\  & LR (0.95) & (0.78) & (0.95) & (0.92) & LR (0.95) & (0.78) & (0.95) & (0.84) \\ \multirow{3}{*}{Perfumery} & SVM, RF & K-NN & SVM, LR & K-NN & SVM, RF, & K-NN & SVM, LR & K-NN \\  & LR (0.93) & (0.85) & (0.93) & (0.84) & LR (0.93) & (0.85) & (0.92) & (0.82) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Proposed model performance comparison of classifiers.