LEADER 00734nam2 22002291i 450 001 990007799230403321 035 $a000779923 035 $aFED01000779923 035 $a(Aleph)000779923FED01 035 $a000779923 100 $a20030801d--------km-y0itay50------ba 200 1 $aPrime considerazioni sul concetto di en-te creditizio e di attivitą bancaria neldecreto legislativo 481/92. 463 0$1001000773719 610 0 $a 701 1$aSantoro,$bVittorio$0115009 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007799230403321 959 $aDDCP 996 $aPrime considerazioni sul concetto di en-te creditizio e di attivitą bancaria neldecreto legislativo 481$9661472 997 $aUNINA DB $aGEN01 LEADER 03850nam 22006255 450 001 9910851997903321 005 20240421124824.0 010 $a9789819724741 024 7 $a10.1007/978-981-97-2474-1 035 $a(CKB)31801763000041 035 $a(MiAaPQ)EBC31289923 035 $a(Au-PeEL)EBL31289923 035 $a(MiAaPQ)EBC31319810 035 $a(Au-PeEL)EBL31319810 035 $a(DE-He213)978-981-97-2474-1 035 $a(EXLCZ)9931801763000041 100 $a20240421d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSentiment Analysis and its Application in Educational Data Mining /$fby Soni Sweta 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (116 pages) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 311 08$a9789819724734 327 $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. 330 $aThe book delves into the fundamental concepts of sentiment analysis, its techniques, and its practical applications in the context of educational data. The book begins by introducing the concept of sentiment analysis and its relevance in educational settings. It provides a thorough overview of the various techniques used for sentiment analysis, including natural language processing, machine learning, and deep learning algorithms. The subsequent chapters explore applications of sentiment analysis in educational data mining across multiple domains. The book illustrates how sentiment analysis can be employed to analyze student feedback and sentiment patterns, enabling educators to gain valuable insights into student engagement, motivation, and satisfaction. It also examines how sentiment analysis can be used to identify and address students' emotional states, such as stress, boredom, or confusion, leading to more personalized and effective interventions. Furthermore, the book explores the integration of sentiment analysis with other educational data mining techniques, such as clustering, classification, and predictive modeling. It showcases real-world case studies and examples that demonstrate how sentiment analysis can be combined with these approaches to improve educational decision-making, curriculum design, and adaptive learning systems. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aComputational intelligence 606 $aData mining 606 $aNatural language processing (Computer science) 606 $aMachine learning 606 $aComputational Intelligence 606 $aData Mining and Knowledge Discovery 606 $aNatural Language Processing (NLP) 606 $aMachine Learning 615 0$aComputational intelligence. 615 0$aData mining. 615 0$aNatural language processing (Computer science) 615 0$aMachine learning. 615 14$aComputational Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aNatural Language Processing (NLP). 615 24$aMachine Learning. 676 $a006.312 700 $aSweta$b Soni$0981441 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910851997903321 996 $aSentiment Analysis and its Application in Educational Data Mining$94266581 997 $aUNINA