# 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 Decision Tree & Skin Make-Up & Positive & 8372 (93.8\%) & 159 (1.8\%) & 394 (4.4\%) \\  & & Neutral & 220 (52.6\%) & 119 (28.4\%) & 79 (19\%) \\  & & Negative & 376 (23.9\%) & 63 (4\%) & 1134 (72.1\%) \\  & Eye Make-Up & Positive & 12,126 (95.1\%) & 146 (1.2\%) & 473 (3.7\%) \\  & & Neutral & 252 (50.3\%) & 140 (27.9\%) & 109 (21.8\%) \\  & & Negative & 376 (15.5\%) & 52 (2.1\%) & 1996 (82.4\%) \\  & Lip Make-Up & Positive & 9794 (94.9\%) & 167 (1.6\%) & 361 (3.5\%) \\  & & Neutral & 240 (50.2\%) & 129 (27\%) & 109 (22.8\%) \\  & & Negative & 380 (21.8\%) & 63 (3.6\%) & 1303 (74.6\%) \\  & Skin Care & Positive & 49,067 (95.3\%) & 864 (1.7\%) & 1564 (3\%) \\  & Neutral & 1157 (43.4\%) & 1010 (37.8\%) & 502 (18.8\%) \\  & Negative & 1621 (19.2\%) & 355 (4.2\%) & 6476 (76.6\%) \\ Hair Care & Positive & 25,672 (95.9\%) & 378 (1.4\%) & 710 (2.7\%) \\  & Neutral & 543 (44.2\%) & 489 (39.8\%) & 197 (16\%) \\  & Negative & 729 (19.6\%) & 128 (3.5\%) & 2855 (76.9\%) \\ Body Care & Positive & 31,034 (96.6\%) & 456 (1.4\%) & 633 (2\%) \\  & Neutral & 611 (50.7\%) & 424 (35.3\%) & 168 (14\%) \\  & Negative & 640 (21.8\%) & 133 (4.6\%) & 2153 (73.6\%) \\ Perfume & Positive & 4079 (93.8\%) & 63 (1.4\%) & 207 (4.8\%) \\  & Neutral & 84 (56\%) & 34 (22.7\%) & 32 (21.3\%) \\  & Negative & 195 (23.1\%) & 24 (3\%) & 622 (73.9\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: DT confusion matrix results for each product group. (20%) neutral product reviews in the correct class, it incorrectly predicted 401 (80%) product reviews in other classes. On the other hand, while the model correctly predicted 2061 (85%) negative data, 363 (15%) were predicted in the wrong classes, 356 of which were in the positive class. When we look at another category, make-up products, the RF model correctly classified 4258 (97.9%) positive comments and predicted only 91 (2.1%) comments in other classes. Although 25 (16.7%) neutral comments were predicted correctly, 125 (83.3%) data were misclassified. Here, the model also showed low success for neutral comments. For the negative class, 624 (74.2%) data were predicted correctly; however, 216 (25.8%) data were assigned to the positive class even though they were negative, and one negative datum was perceived as neutral. Table 8 shows the confusion matrix results showing the classification success of the LR model, the last algorithm used in the study. As with the performance of all other algorithms, the logistic regression model showed robust performance in predicting positive comments; however, its performance decreased in the neutral and negative classes. For example, the LR model successfully predicts positive and negative classes in the skin make-up category. The model predicted 8801 (98.6%) positive product reviews in the correct class and classified 124 (1.4%) reviews in other classes. For the neutral class, 63 (15%) reviews were predicted in the correct class, while 355 (85%) data were distributed into positive and negative classes. Regarding the negative classification success, 1279 (81.3%) data were predicted in the correct class, while 294 (8.7%) were predicted in the wrong classes. It is seen that the success of predicting the positive and negative class for the eye make-up category is also high. While 12,549 (98.4%) positive comments were correctly classified, 179 (1.4%) data were incorrectly predicted as negative, and 17 (0.2%) data were incorrectly predicted as neutral. While 99 (19.8%) neutral data were predicted in the correct class,