# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
###### Abstract  
E-commerce sites have generated large amounts of unstructured data as they allow millions of users to generate product reviews. Thus, although there have been significant improvements in the characteristics of big data, such as speed and volume, developing various analysis techniques to monitor, understand, and extract useful information from this web-based data has become challenging. This study aims to analyze cosmetic products on a Turkish-based e-commerce website with sentiment analysis and to create a new domain-specific Turkish sentiment dictionary model with manual labeling. In the study, a Turkish sentiment dictionary consisting of 65,378 words was created by manually labeling 875,455 product reviews for 24 cosmetic brands sold on the Turkey-based trendyol e-commerce site, and sentiment analysis was performed using this dictionary. The dataset, divided into seven product groups, was analyzed using K-NN, SVM, DT, RF, and LR algorithms to address three classification problems. The algorithms were evaluated with comparative analysis using accuracy, precision, recall, and f-1 score metrics. SVM gave the highest performance result with over 93% accuracy, 92% precision, 93% recall, and a 91% f-1 score in all product groups. The dictionary model created for the cosmetics industry in the study helps businesses and researchers to use their resources more efficiently and save time by performing fast and low-cost analyses on large datasets of product reviews. Moreover, by analyzing customer feedback, brands can offer long-lasting and environmentally friendly products that align with customers' feelings. Thus, businesses have the opportunity to develop or improve products. machine learning; natural language processing; sentiment analysis 2025  
1  
<ccs2020> <concept>  
words, jargon, and forms of expression that consumers use while attributing and revealing their sentiments towards cosmetic products. This study, which analyzes web-based text data generated by users in the digital environment, is important in analyzing Turkish texts. In addition, the study reveals its originality because no other study uses Turkish product reviews as a dataset in the cosmetics industry. There is a need for automated, fast, and easy methods to be used in business analytics, brand reputation, and strategy development from the large amount of data that consumers produce in order to make their voices heard by brands and businesses, make suggestions to other customers, or share their experiences. Sentiment analysis and various machine learning methods are among the research tools used to meet these needs. This method is used to extract people's sentiments from web-based data. It was born as an extension of data mining using natural language processing techniques, and it is important in natural language processing, data and text mining, and big data. Sentiment analysis, mainly performed by processing, analyzing, and interpreting textual data, is an important tool in detecting people's emotions and predicting them for the future. This approach is important and offers advantages for individuals, businesses, various institutions, and even governments. Within the scope of the objectives of the thesis study, the following research questions were addressed:  
RQ1: Can sentiment analysis be performed with a large Turkish textual dataset? RQ2: Can a sentiment dictionary with high accuracy and performance be created based on Turkish product reviews? RQ3: How do different machine learning algorithms evaluate sentiment analysis performance on Turkish cosmetic product reviews? Business that benefit from sentiment analysis have advantages such as understanding customer behavior, following market trends, or learning about their competitors' situation. It is a suitable technology for perceiving and analyzing a consumer's behavior [1]. In addition to saving business time, it provides an understanding of what customers expect from the business and its products [2].