Sentiment analysis is increasingly pivotal in natural language processing (NLP), crucial for deciphering public opinions across diverse sectors. This research conducts a comparative examination of rule-based and machine learning (ML) methods in sentiment analysis, specifically targeting the Kazakh language. Given the Kazakh language's limited exposure in computational linguistics, the study meticulously evaluates datasets from news articles, literature, and Amazon product reviews, aiming to compare the efficiency, adaptability, and overall performance of these distinct approaches. Employing a detailed set of evaluation metrics such as accuracy, precision, recall, and computational efficiency, the study provides a comprehensive analysis of the strengths and limitations of rule-based techniques versus ML models like Logistic Regression, Multinomial Naive Bayes, Decision Trees, Random Forest, and XGBoost. The findings suggest rule-based methods excel in identifying nuanced emotional expressions within literary texts, while ML models demonstrate superior adaptability and robustness, particularly effective in handling the linguistic variations found in news and reviews. Despite the strengths identified, the study also reveals significant limitations of the rule-based approach, especially in broader contexts beyond literary analysis. This highlights an imperative for future research to integrate sentiment dictionaries or domain-specific lexicons that cater to a wider array of linguistic styles, potentially enhancing sentiment analysis tools' applicability in Kazakh and similar less-studied languages. This investigation contributes significantly to the sentiment analysis discourse, offering invaluable insights for both researchers and practitioners by elucidating the complexities of applying NLP technologies across diverse linguistic landscapes, thus advancing the understanding and methodologies of sentiment analysis in the Kazakh language context.

This paper examines how academic staff access and use e-books, and what features they expect from a future digital library platform. Based on survey results, the study reveals that most respondents rely on informal sources, while official databases are used less frequently. At the same time, there is strong demand for key functionalities such as access to a wide collection of e-books, quiz generation, personalized recommendations, and tools for tracking student performance. In addition, the paper provides a comparative review of existing platforms—Perlego, QuizBot, Bookmate, Khan Academy, and Scopus—highlighting both their strengths and limitations, particularly with respect to language support and integration. Taken together, the survey findings and platform analysis underscore the need for a unified adaptive platform that connects publishers, libraries, and users, thereby enhancing accessibility, interoperability, and effectiveness in digital learning.