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@article{179729,
        author = {Agalya M and Ayisha Parveen A and Kaviya V and Keerthana J and Niroshini R},
        title = {Applying AI And Semantic Analysis To Identify And Address Harmful Online Comments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8377-8381},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179729},
        abstract = {This project focuses on developing a robust 
machine learning model for toxic comment 
classification, aiming to enhance user experience and 
safety in online environments. The primary objective is 
to create a model that accurately classifies comments as 
toxic or non-toxic based on their content, thereby 
facilitating effective moderation and reducing the 
spread of harmful language. The preprocessing stage 
includes text normalization, tokenization, and the 
removal of stopwords and irrelevant characters to 
prepare the data for model training. Additionally, we 
implement techniques to address class imbalance, 
ensuring that the model is not biased toward the 
majority class. For model development, we explore a 
range of machine learning algorithms, including 
logistic regression, support vector machines (SVM), 
and deep learning models such as convolutional neural 
networks (CNN) and recurrent neural networks 
(RNN).},
        keywords = {Toxic comment classification, Machine  learning model, Text normalization, Tokenization,  Stopwords removal, Class imbalance, Logistic  regression, Support vector machines (SVM), Deep  learning models, Convolutional neural networks  (CNN),  Recurrent  Performance metrics.},
        month = {May},
        }