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@article{184621,
        author = {VARALAKSHMI N and Dr. HANUMANTAPPA M},
        title = {SENTIMENTAL ANALYSIS IN ONLINE LEARNING ENVIRONMENT : A REVIEW},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {1970-1977},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184621},
        abstract = {The surge in online learning platforms has led to a significant increase in user-generated feedback through course reviews and ratings. These reviews serve as a valuable source for improving course quality, teaching methods, and learner engagement. This survey paper provides a comprehensive review of existing online course review systems, focusing on their underlying methodologies, including sentiment analysis, machine learning models, and feedback classification techniques. The paper presents a taxonomy of approaches used to process and analyze student reviews, highlights existing challenges such as review bias and data sparsity, and outlines future directions in developing intelligent and interpretable review systems. This survey aims to help researchers and educators understand the current landscape and identify potential areas for improvement.},
        keywords = {Course review and ratings, Sentimental Analysis, Machine learning models, data Sparsity, interpretable review system.},
        month = {September},
        }