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020 _a9789819724741
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024 7 _a10.1007/978-981-97-2474-1
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
100 1 _aSweta, Soni.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_975462
245 1 0 _aSentiment Analysis and its Application in Educational Data Mining
_h[electronic resource] /
_cby Soni Sweta.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXXI, 97 p. 8 illus., 6 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
505 0 _aChapter 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 _aThe 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.
650 0 _aComputational intelligence.
_964052
650 0 _aData mining.
_96941
650 0 _aNatural language processing (Computer science).
_965131
650 0 _aMachine learning.
_955059
650 1 4 _aComputational Intelligence.
_964052
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aNatural Language Processing (NLP).
_966164
650 2 4 _aMachine Learning.
_955059
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819724734
776 0 8 _iPrinted edition:
_z9789819724758
830 0 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
_974073
856 4 0 _uhttps://doi.org/10.1007/978-981-97-2474-1
912 _aZDB-2-INR
912 _aZDB-2-SXIT
999 _c59250
_d59250