Content area
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.
Details
Learning Motivation;
Long Term Memory;
Language Acquisition;
Memory;
Natural Language Processing;
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;
Intelligence;
Data Analysis;
Educational Facilities Improvement
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