03850nam 22006255 450 991085199790332120240421124824.0978981972474110.1007/978-981-97-2474-1(CKB)31801763000041(MiAaPQ)EBC31289923(Au-PeEL)EBL31289923(MiAaPQ)EBC31319810(Au-PeEL)EBL31319810(DE-He213)978-981-97-2474-1(EXLCZ)993180176300004120240421d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierSentiment Analysis and its Application in Educational Data Mining /by Soni Sweta1st ed. 2024.Singapore :Springer Nature Singapore :Imprint: Springer,2024.1 online resource (116 pages)SpringerBriefs in Computational Intelligence,2625-37129789819724734 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.SpringerBriefs in Computational Intelligence,2625-3712Computational intelligenceData miningNatural language processing (Computer science)Machine learningComputational IntelligenceData Mining and Knowledge DiscoveryNatural Language Processing (NLP)Machine LearningComputational intelligence.Data mining.Natural language processing (Computer science)Machine learning.Computational Intelligence.Data Mining and Knowledge Discovery.Natural Language Processing (NLP).Machine Learning.006.312Sweta Soni981441MiAaPQMiAaPQMiAaPQ9910851997903321Sentiment Analysis and its Application in Educational Data Mining4266581UNINA