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Fuzzy Sentiment Analysis for Improving German Learning in Corpus-Based Deep Learning Approaches

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Publicat a:International Journal of Web-Based Learning and Teaching Technologies vol. 20, no. 1 (2025), p. 1-23
Autor principal: Dong, Qi
Publicat:
IGI Global
Matèries:
Students
Tutoring
Deep learning
Emotional states
Technology
Educational technology
Emotions
Sentiment analysis
Learning
Motivation
Corpus analysis
Effectiveness
Classifiers
Emotional factors
Language acquisition
Feedback
Corpus linguistics
Fuzzy logic
Intelligence
Artificial intelligence
User satisfaction
Natural language processing
Satisfaction
Customization
Literature Reviews
Learning Motivation
Long Term Memory
Memory
Student Motivation
Language Processing
Algorithms
Grammar
Language Usage
Educational Resources
Language Skills
Distance Education
Influence of Technology
Intelligent Tutoring Systems
Language Research
Addition
Learning Processes
Learning Experience
Native Language
Native Speakers
Data Analysis
Educational Facilities Improvement
Accés en línia:Citation/Abstract
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Resum:This study aims to explore how to optimize corpus-based deep learning methods by introducing fuzzy sentiment analysis technology to improve the effectiveness and interactivity of German learning. By building an intelligent tutoring system that can perceive the emotional state of German learners, the effectiveness and interactivity of learning can be improved. Experimental results show that the fuzzy sentiment classifier has significant advantages in language skill improvement, user satisfaction, learning motivation, and sustained engagement. Fuzzy sentiment analysis technology can capture and process learners' emotional states more delicately, provide personalized feedback and support, and identify individual learning patterns and preferences based on long-term accumulated data, thereby recommending customized learning paths.
ISSN:1548-1093
1548-1107
DOI:10.4018/IJWLTT.383940
Font:Engineering Database