# 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{3-5}  & & & **Positive** & **Neutral** & **Negative** \\ \hline \multirow{3}{*}{Random Forest} & Skin Make-Up & Positive & 8756 (98.1\%) & 24 (0.3\%) & 145 (1.6\%) \\  & & Neutral & 261 (62.4\%) & 95 (22.7\%) & 62 (14.9\%) \\  & & Negative & 416 (26.4\%) & 7 (0.5\%) & 1150 (73.1\%) \\  & Eye Make-Up & Positive & 12,471 (97.8\%) & 35 (0.3\%) & 239 (1.9\%) \\  & & Neutral & 317 (63.3\%) & 100 (20\%) & 84 (16.7\%) \\  & & Negative & 356 (14.7\%) & 7 (0.3\%) & 2061 (85\%) \\  & Lip Make-Up & Positive & 10,173 (98.6\%) & 20 (0.2\%) & 129 (1.2\%) \\  & & Neutral & 317 (66.4\%) & 96 (20\%) & 65 (13.6\%) \\  & & Negative & 451 (25.8\%) & 6 (0.3\%) & 1289 (73.9\%) \\  & & 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\%) \\  & & Positive & 26,388 (98.6\%) & 62 (0.2\%) & 305 (1.2\%) \\  & & Neutral & 646 (52.6\%) & 432 (35.1\%) & 151 (12.3\%) \\  & & Negative & 724 (19.5\%) & 34 (0.9\%) & 2954 (79.6\%) \\ Body Care & Positive & 31,819 (99\%) & 81 (0.3\%) & 223 (0.7\%) \\  & & Neutral & 717 (59.6\%) & 343 (28.5\%) & 143 (11.9\%) \\  & & Negative & 668 (22.8\%) & 19 (0.7\%) & 2239 (76.5\%) \\ Perfume & Positive & 4258 (97.9\%) & 11 (0.3\%) & 80 (1.8\%) \\  & Neutral & 90 (60\%) & 25 (16.7\%) & 35 (23.3\%) \\  & Negative & 216 (25.7\%) & 1 (0.1\%) & 624 (74.2\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: RF confusion matrix results for each product group. 310 (61.9%) neutral data were predicted as positive, and 92 (18.3%) data were incorrectly predicted as negative. A total of 1380 (79%) negative data were correctly predicted; however, 366 (21%) were misclassified, 349 (20%) of which were positive. In the last stage of the study, Table 9 presents the classification performance of each machine learning model with accuracy, precision, recall, and f-1 score metrics and a performance comparison of the algorithms. When all categories are analyzed, each classifier has acceptable and high-classification performance values. However, when compared, the support vector machine has shown high classification performance in all product groups. Although decision tree, random forest, and logistic regression have successful performance measurements, they are relatively behind the support vector machine. On the other hand, K-nearest neighbor did not perform as well as the other classifiers and gave the lowest results in the comparison. Among machine learning approaches, the algorithms that performed the highest and lowest in all product groups are shown in Table 10. When accuracy rates are examined, the highest score is obtained by SVM, and K-NN obtains the lowest score. On the other hand, in the eye make-up, lip make-up, general make-up, skin care, body care, and perfumery groups, SVM has the highest score. Additionally, with LR and RF in body care and LR in perfumery. Despite its ability to achieve maximum prediction accuracy when working with extensive and complex data and its tendency to perform better than other algorithms [64], the SVM algorithm has been an important reason for the method to give the best result in the dataset. Table 10 shows that the SVM algorithm has the most accuracy in all product groups and superior performance in other metrics. SVM has come to the forefront by optimizing class boundaries and giving high accuracy rates. At the same time, SVM can obtain true positives and minimize false negatives by having precision, recall, and f-1 score values in all product groups.