# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
## 2 Literature Review  
This study focuses on meaningful information extraction and model building with sentiment analysis and machine learning approaches from big data consisting of Turkish product reviews in the cosmetics industry. Various models have been constructed and applied in studies conducted in the cosmetics industry using the sentiment analysis research method. [PERSON] et al. performed aspect-based sentiment analysis on a dataset containing 16,227 Vietnamese reviews of lipstick products. This study evaluated model performance by comparing single-task and multi-task learning with a deep learning approach. They achieved a success rate of 98.09% with the BIGRU + Conv1D model. In addition, they determined that the classification performance of the proposed model specific to the Vietnamese language works with a 91.01% f-1 score success rate [7]. [PERSON] and [PERSON] conducted a dimension-based sentiment analysis study using a support vector machine algorithm. Semantic similarity and TF-IDF weighting were added to cosmetic product reviews in Indonesia. In the study, which includes a dataset of sunscreen, tonic, serum, essence, scribs, and exfoliating product groups, price, packing, and scent dimensions were extracted, and the model's performance was evaluated for each aspect. From the test results, the accuracy rates were 93%, 92%, and 86% for the price, packing, and scent aspects, respectively [8]. [PERSON] and [PERSON] conducted a sentiment analysis study on consumer reviews of BB cream products and compared competing products. The researchers compared the competitors with datasets including 4335 reviews of 42 products in the Korean product group and 6001 reviews of 194 products in the other product group. They used the discriminative features of words using word frequency analysis, LSA, and L-LDA. In the last stage, the proposed model was supported by a t-test, and the study concluded that Korean products are more advantageous in competition because they have a higher average [9]. [PERSON] et al. performed aspect-based sentiment analysis with 5053 Indonesian product reviews in toners, serums and essences, scribs and exfoliators, and sun protection cosmetics. This study used TF-IDF and n-gram feature extraction methods to extract price, packing, and fragrance dimensions, and classification performances were evaluated with the random forest algorithm. This study obtained 90.48% accuracy, 87.27% precision, 70.13% recall, and 71.77% f-1 score values [10]. [PERSON] et al. conducted a sentiment analysis study using a dataset of 9899 English-language product reviews of skin care products from Drunk Elephant, Origins, Belief, Laneige, and Glam Glow brands. Performing a comparative analysis with naive Bayes, KNN, SVM, decision tree, and deep learning algorithms, the researchers achieved the best performance with deep learning and decision tree, with an accuracy close to 80% and an f-1 score of 60% [11]. [PERSON] performed sentiment analysis using a dataset of 35,617 customer reviews of 26 top global cosmetic brands and evaluated relative customer satisfaction through TF-IDF analysis. It is predicted that the approach proposed in the study can be applied to realize or improve customer satisfaction with cosmetic brands [12]. [PERSON] et al. developed a model that classifies 110,097 product reviews of the top 26 cosmetic brands by sentiment analysis and reveals sentiment-attributed features by applying latent Dirichlet allocation [13]. [PERSON] and [PERSON] presented a new approach for sentiment classification from 12,000 Chinese cosmetic product reviews (WOMs). The authors created a dictionary model consisting of feature words and contextual words, evaluated the model's performance using SVM, J48 decision tree, and multilayer perceptron, and achieved a high success rate [14]. [PERSON] et al. conducted a sentiment analysis study with a dataset of more than 700,000 Indonesian cosmetic product reviews, addressing the triple classification problem.