03898nam a22005535i 4500 978-981-97-2474-1 DE-He213 20251030112002.0 cr nn 008mamaa 240421s2024 si | s |||| 0|eng d 9789819724741 978-981-97-2474-1 10.1007/978-981-97-2474-1 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Sweta, Soni. author. aut http://id.loc.gov/vocabulary/relators/aut 75462 Sentiment Analysis and its Application in Educational Data Mining [electronic resource] / by Soni Sweta. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XXI, 97 p. 8 illus., 6 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Computational Intelligence, 2625-3712 Chapter 1: Sentiment Analysis in Natural Language Processing -- Chapter 2: An Overview of Educational Data Mining -- Chapter 3: Impact of Sentiment Analysis in Education Sector -- Chapter 4: Techniques and Approaches in Sentiment Analysis -- Chapter 5: Machine Learning with Sentiment Analysis -- Chapter 6: Incorporation of Sentiment Analysis with Educational Data Mining -- Chapter 7: Preformation Evaluation using Sentiment Analysis. The book delves into the fundamental concepts of sentiment analysis, its techniques, and its practical applications in the context of educational data. The book begins by introducing the concept of sentiment analysis and its relevance in educational settings. It provides a thorough overview of the various techniques used for sentiment analysis, including natural language processing, machine learning, and deep learning algorithms. The subsequent chapters explore applications of sentiment analysis in educational data mining across multiple domains. The book illustrates how sentiment analysis can be employed to analyze student feedback and sentiment patterns, enabling educators to gain valuable insights into student engagement, motivation, and satisfaction. It also examines how sentiment analysis can be used to identify and address students' emotional states, such as stress, boredom, or confusion, leading to more personalized and effective interventions. Furthermore, the book explores the integration of sentiment analysis with other educational data mining techniques, such as clustering, classification, and predictive modeling. It showcases real-world case studies and examples that demonstrate how sentiment analysis can be combined with these approaches to improve educational decision-making, curriculum design, and adaptive learning systems. Computational intelligence. 64052 Data mining. 6941 Natural language processing (Computer science). 65131 Machine learning. 55059 Computational Intelligence. 64052 Data Mining and Knowledge Discovery. Natural Language Processing (NLP). 66164 Machine Learning. 55059 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819724734 Printed edition: 9789819724758 SpringerBriefs in Computational Intelligence, 2625-3712 74073 https://doi.org/10.1007/978-981-97-2474-1 ZDB-2-INR ZDB-2-SXIT 59250 59250