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Sentiment analysis and its application in educational data mining

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sentiment analysis and its application in educational data miningby Soni Sweta.
Author:	Sweta, Soni.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xxi, 97 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Sentiment analysis.
Online resource:	https://doi.org/10.1007/978-981-97-2474-1
ISBN:	9789819724741$q(electronic bk.)

Sentiment analysis and its application in educational data mining
Sweta, Soni.

Sentiment analysis and its application in educational data mining[electronic resource] /by Soni Sweta. - Singapore :Springer Nature Singapore :2024. - xxi, 97 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computational intelligence,2625-3712. - SpringerBriefs in computational intelligence..

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.

ISBN: 9789819724741$q(electronic bk.)

Standard No.: 10.1007/978-981-97-2474-1doiSubjects--Topical Terms:

892169
Sentiment analysis.

LC Class. No.: QA76.9.S57

Dewey Class. No.: 006.312

Sentiment analysis and its application in educational data mining
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505 0 $a 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.
520 $a 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.
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