# 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{3-6}  & & & **Positive** & **Neutral** & **Negative** \\ \hline \multirow{4}{*}{Support Vector Machine} & Skin Make-Up & Positive & 8816 (98.8\%) & 4 (0.0\%) & 105 (1.2\%) \\  & & Neutral & 290 (69.4\%) & 61 (14.6\%) & 67 (16\%) \\  & Eye Make-Up & Negative & 295 (18.7\%) & 6 (0.4\%) & 1272 (80. 9\%) \\  & Eye Make-Up & Positive & 12,577 (98.7\%) & 10 (0.1\%) & 158 (1.2\%) \\  & & Neutral & 338 (67.5\%) & 88 (17.6\%) & 75 (14.9\%) \\  & & Negative & 316 (13\%) & 3 (0.1\%) & 2105 (86.9\%) \\  & Lip Make-Up & Positive & 10,229 (99.1\%) & 4 (0.0\%) & 89 (0.9\%) \\  & & Neutral & 341 (71.4\%) & 46 (9.6\%) & 91 (19\%) \\  & & Negative & 377(21.5\%) & 4 (0.2\%) & 1365 (79.2\%) \\  & Skin Care & Positive & 50829 (98.7\%) & 80 (0.2\%) & 586 (1.1\%) \\  & & Neutral & 1334 (50\%) & 870 (32.6\%) & 465 (17.4\%) \\  & & Negative & 1126 (13.3\%) & 56 (0.7\%) & 7270 (86\%) \\  & Hair Care & Positive & 26,515 (98.1\%) & 27 (0.1\%) & 218 (0.8\%) \\  & Neutral & 686 (55.8\%) & 414 (33.7\%) & 129 (10.5\%) \\  & & Negative & 603 (16.2\%) & 21 (0.6\%) & 3088 (83.2\%) \\ Body Care & Positive & 31,914 (99.3\%) & 36 (0.1\%) & 173 (0.5\%) \\  & Neutral & 749 (62.2\%) & 305 (25.4\%) & 149 (12.4\%) \\  & & Negative & 534 (18.2\%) & 11 (0.4\%) & 2381 (81.4\%) \\ Perfume & Positive & 4279 (98.4\%) & 0 & 70 (1.4\%) \\  & Neutral & 104 (69.3\%) & 14 (9.3\%) & 32 (21.3\%) \\  & Negative & 157 (18.7\%) & 0 & 684 (81.3\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SVM confusion matrix results for each product group. The confusion matrix results evaluating the classification prediction success of the decision tree algorithm are presented in Table 6. The DT algorithm successfully predicts positive classes, as seen in the classification performance of other machine learning approaches. Although the positive classification prediction success was high in all groups, confusion in the model was observed in neutral and negative classes. For instance, it was observed that the model's success in recognizing the positive class was high in the skin care products group. While 49,067 (95.3%) data were correctly identified in the positive class, 2428 (4.7%) data were predicted in other classes. In the skin care products group, it was observed that the model's success in recognizing the positive class was high. In the neutral class, 1010 (37.8%) data were correctly predicted, but 1659 (62.2%) data were incorrectly assigned to other classes. When we look at the prediction success of negative product reviews with DT, 2855 data were correctly predicted (76.9%) and 847 (25.1%) reviews were incorrectly predicted. The model successfully predicts positive and negative product reviews in the body care products group. A total of 31,034 (96.6%) reviews were correctly predicted in the positive class, 1089 (3.4%) were distributed to other classes. However, when considered as a ratio, the prediction success of the positive class is high. While 424 (35.3%) neutral examples were predicted correctly, 779 (64.7%) comments were incorrectly assigned to other classes. When we look at the success of the negative class, 2153 (73.6%) comments were predicted in the correct class, and 773 (26.4%) comments were predicted in the wrong classes. The confusion matrix results of the random forest algorithm in Table 7 conclude that the classification success is high for positive and negative product reviews. However, the classification success is low for neutral reviews. For instance, in the eye make-up products group, it is seen that the RF model again made successful predictions with 12,471 (97.8%) positive comments assigned to the correct class. On the other hand, 274 (2.2%) positive comments were incorrectly assigned to other classes. Although the model predicted 100