Full Text PDF
ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 9 (2026): Volume 10, Issue 9, September 2026 | Pages: 164-168
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 28-09-2026
Aware Comment Moderation System that uses TF-IDF based classifiers, BERT-based language model, and rules. The models used for providing quick prediction include Logistic Regression, SGD Classifier, and Naïve Bayes, and BERT is used for those comments whose meaning depends highly on the context. FastAPI serves as an interface between prediction service and web interface while the database maintains the user, post, comment, score, and moderation. The comment is then classified as Non-Toxic, Mild Toxic, or Severe Toxic before any action is taken against the comment like approving, sending for review, or rejecting.
Aware Moderation, BERT, Natural Language Processing, TF-IDF, FastAPI, Text Classification
K. Sreeveda, B. Varshitha, P. Abhigna, G. Indu Maithreyee, & B. Nashwitha. (2026). Context Aware Comment Moderation System. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(9), 164-168. Article DOI https://doi.org/10.47001/IRJIET/2026.109017
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL-HLT, 2019.
A.Schmidt and M. Wiegand, “A Survey on Hate Speech Detection using Natural Language Processing,” SocialNLP, 2017.
T. Davidson, D. Warmsley, D. Macy, and I. Weber, “Automated Hate Speech Detection and the Problem of Offensive Language,” ICWSM, 2017.
P. Badjatiya, S. Gupta, M. Gupta, and V. Varma, “Deep Learning for Hate Speech Detection in Tweets,” WWW, 2017.
Z. Zhang, D. Robinson, and J. Tepper, “Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network,” ESWC, 2018.
J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global Vectors for Word Representation,” EMNLP, 2014.
A.Vaswani et al., “Attention Is All You Need,” NeurIPS, 2017.
F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, 2011.
T. Wolf et al., "Hugging Face Transformers: State-ofthe-art Natural Language Processing," EMNLP, 2020.