## 1 Introduction  
Sentiment analysis has emerged as a rapidly advancing research area in natural language processing (NLP), driven by the increasing need to analyze user-generated content [1]. In contemporary society, individuals exhibit a strong inclination to understand the emotions and thoughts of others who have had similar experiences, even when making relatively simple decisions. Studies indicate that 84% of internet users perceive online reviews as personal recommendations, and 68% form decisions after reading only one to six reviews. Consequently, the proliferation of the internet and social media has transformed user-generated online reviews into a highly valuable data source, characterized by its accessibility and diversity. These reviews play a critical role not only in guiding individual decisions but also in enabling companies to identify user preferences and trends. Moreover, sentiment analysis facilitates decision-making based on societal emotional trends by examining the rapidly expanding volume of user-generated content on digital platforms such as social media, blogs, and forums. Users' opinions about products and services provide businesses with opportunities to analyze this content and monitor market trends. Moreover,organizations and governments can make strategic decisions informed by emotional trends derived from social media posts [2]. Sentiment analysis methods process texts to identify positive, negative, or neutral sentiments expressed in various forms, such as product reviews, movie critiques, or social media posts. This capability is particularly critical for e-commerce companies that aim to understand online trends, enhance product and service quality, and respond effectively to customer preferences. For example, sentiment analysis can determine which products are more popular, assess audience reactions to a movie, or identify trending music [3]. As the volume of data on the internet continues to grow, sentiment analysis has become a crucial tool for enhancing decision-making processes and gaining insights into societal emotional and cognitive trends. However, while significant progress has been achieved in sentiment analysis for widely spoken languages such as English, unique challenges arise when working with Turkish due to its linguistic characteristics. Turkish, as an agglutinative language, forms words by adding a variety of suffixes to root words. This morphological structure results in a vast range of word forms, creating complex linguistic patterns that can impact the accuracy of sentiment classification [4]. For instance, a single lemma in Turkish may have hundreds of surface forms due to inflectional and derivational morphology, significantly increasing vocabulary size and leading to data sparsity in statistical models. Additionally, Turkish exhibits extensive vowel harmony, consonant alternations, and long words formed by suffix concatenation, where a single misspelled word can disrupt sentence-level comprehension [5]. Additionally, the flexible syntax of Turkish introduces further complexities to text processing tasks such as sentiment analysis, making it more challenging to extract accurate insights from Turkish texts. The flexible syntax of Turkish introduces further complexities to text processing tasks such as sentiment analysis, as word order is not fixed and can vary significantly depending on context and emphasis [6]. Furthermore, the challenges of processing informal Turkish social media texts are magnified due to the prevalence of missing vowels, diacrities, emoticons, slang, and regionally influenced spelling errors. Another notable limitation is the scarcity of large-scale labeled datasets for Turkish, in contrast to English and other widely spoken languages. This shortage poses a significant challenge for training robust models using machine learning and deep learning techniques. To address these challenges, this study introduces a balanced and extensive dataset to advance the field of Turkish sentiment analysis.