# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
## 4 Results
For example, while 'harika (great)' is a positive word, the sentences 'Harika bir' urun oldugunu soyleyemeyecegim (I do not think it is a great product)' or 'Harika kapattingm soyleyemeyecegim (I cannot say that it covered perfectly)' carry negative sentiments. At this stage, 10,040 words and phrases were made suitable for analysis by normalization. Therefore, normalization is an important step of the study. Examples of normalized words and phrases are presented in Table 3.  
After the preprocessing, context-specific words and phrases were taken into account while sentiment labeling was performed, and it was aimed to increase the accuracy of the analysis. Since the cosmetics industry is a niche area, it is predicted that accurate results will be obtained with the sentiment dictionary prepared for special purposes. For example, words like yogun (intense) or parlama (shine) can express a sentiment-laden feature in cosmetic product reviews. While the word 'yogun' has a positive meaning for an eye make-up product, it may have a negative meaning for a skin care product. The word 'parlama' may have a positive meaning for a body care product but a negative meaning for a skin care product. Thus, it tried to capture all the nuances in the product reviews with manual labeling and custom dictionary creation. At the same time, the unique terminology of cosmetic products and consumer expectations could be considered in the context of jargon originating from specific expressions frequently used in consumer evaluations of cosmetic products. Consumers may focus on specific words and express opinions with detailed information about cosmetic products. For example, in terms of detailed consideration of specific expressions such as 'kapatclik' (concealment),'sabitlenme' (sticking), 'kalclik' (persistence), 'portakal kabugu' (orange peel), the creation of a unique sentiment dictionary with manual labeling enabled the inclusion of meaningful expressions in the model. Thus, a sentiment dictionary consisting of 65,378 words at the word and phrase level was created by manual labeling. The cosmetic product reviews dataset in each category and product group was divided into 80% training data and 20% test data to be subjected to positive, neutral, and negative classification. Then, the TF-IDF vectorizer method was used to convert the textual data into numerical form and make it suitable for machine learning algorithms. By calculating TF-IDF ratios, the frequency levels and usage percentages of the words in each dataset were calculated. After all these stages, the classification success of manually labeled data with machine learning models was tested with system evaluation. The confusion matrix results of the K-NN model are presented in Table 4. When the test results are analyzed, the K-NN algorithm's prediction success is high in positive class predictions in each product category. In all product categories, the prediction success of K-NN in the neutral class was lower than in the positive and negative classes. To illustrate, in the lip make-up product category, 10,093 (97.8%) data were correctly predicted in the positive class, and 229 (2.2%) data were incorrectly assigned to other classes. In addition, 69 (14.4%) instances were correctly recognized in the neutral class, 409 (85.6%) were incorrectly predicted in other classes; 549 (31.4%) negative classes were correctly assigned, and 1197 (68.6%) negative data were distributed to other classes. In this case, it is seen that the model fails to learn the neutral class, while the success rate is high in the positive class. The confusion matrix results showing the success of the SVM model in predicting the classes are given in Table 5. The SVM algorithm achieved very high success in predicting positive classes in each product category. However, it is seen that the model has difficulty in predicting neutral and negative classes. Especially the neutral class was the weak side of the model in all categories.