Here are the dataset, the build steps from a simple baseline to a transformer, the metrics for six uneven labels, the bias problem examiners ask about and the scope for each level.
- Dataset: the Jigsaw challenge set, 159,571 labelled Wikipedia comments for training and 63,978 scored test comments.
- Six labels, any number per comment: toxic, severe toxic, obscene, threat, insult and identity hate.
- Build a baseline first: TF-IDF with logistic regression trains in minutes and is hard to beat by much.
- Report each label separately. Rare labels such as threat hide behind an average.
- Check for bias. Models can learn to flag harmless comments that only mention an identity group.
That is what the task needs. Mask or paraphrase examples in your report and slides, and warn your audience before a live demo.
Which dataset should you use for toxic comment classification?
Use the Jigsaw Toxic Comment Classification Challenge data on Kaggle. The comments come from Wikipedia talk pages and were labelled by human raters.
| What | Detail |
|---|---|
| Training comments | 159,571, each with six yes or no labels |
| Scored test comments | 63,978. The test file also holds rows that were never scored; drop them before you evaluate |
| Labels | Toxic, severe toxic, obscene, threat, insult, identity hate |
| Balance | Most comments have no label, and threat and identity hate are rare |
| Licence | CC0 |
How do you build a toxic comment classifier step by step?
- Clean lightly. Remove markup and links, but keep punctuation and capital letters, which carry signal in angry text.
- Build the baseline. TF-IDF on words and on character n-grams, then one logistic regression per label.
- Train a neural model. A bidirectional LSTM or GRU over word embeddings, with six sigmoid outputs.
- Fine-tune a transformer. DistilBERT is small enough for a free GPU session; BERT or RoBERTa if you have more time.
- Set a threshold for each label on a validation set, because one cut-off does not suit a common label and a rare one.
- Study the errors. Read the false alarms and the misses, and group them: sarcasm, quoted abuse, identity terms, spelling tricks.
- Build the demo. A text box that returns the six scores and highlights the words behind them, using LIME or attention.
Which metrics should you report for six labels?
The original competition ranked entries by ROC-AUC averaged over the six labels, so report that number to compare with others. Then go further:
- ROC-AUC, precision, recall and F1 for each label, in one table.
- Precision-recall curves for the rare labels, where ROC-AUC looks better than the model is.
- Training time and prediction time, so the baseline and the transformer are compared fairly.
- A bias table: scores on harmless sentences that mention religion, gender or nationality.
What mistakes cost marks in this project?
- Using softmax. The labels are not exclusive; use a sigmoid per label.
- Reporting plain accuracy. Predicting no label for every comment already scores high.
- Evaluating on unscored test rows. Filter them out first.
- Over-cleaning. Stripping all punctuation, case and stop words removes cues the model needs.
- Ignoring bias. Say what your model does with identity terms, and what you tried to reduce it.
How does the scope change for Diploma, B.Tech and M.Tech?
| Level | Scope | What to show |
|---|---|---|
| Diploma | TF-IDF and logistic regression for the single toxic label | A web form that marks a comment as acceptable or toxic |
| B.Tech / B.E. | All six labels: the baseline, an LSTM and DistilBERT compared, with thresholds per label | A per-label results table and a demo that highlights words |
| M.Tech / M.E. | Bias-aware evaluation and mitigation, code-mixed Hindi and English data with multilingual models, or how faithful the explanations are | A base paper reproduced, one measured extension and a paper in IEEE format |
For more language projects, including Indian-language data, see the NLP project ideas in our machine learning list. Polytechnic students can start from the Diploma projects page.
What goes in the report, and which viva questions come up?
Cover the problem, related work, the data and its labels, each model, the per-label results, the error analysis and the limits. If your course needs a paper, follow the IEEE paper format. Expect these questions:
- Why is this multi-label and not multi-class?
- What does TF-IDF measure, and why add character n-grams?
- Why does the threat label score worse than the toxic label?
- How does a transformer read a sentence differently from an LSTM?
- Where would your model be unfair, and what did you do about it?
Other single-topic guides: AI-generated image detection and customer churn prediction.
This guide is a plan to discuss with your guide, and the figures in it come from the linked dataset pages and papers, not from our own work. Our 8 delivered case studies are M.Tech and M.E. projects, each shown with its real paper pages, screens and results.
Frequently asked questions
Which dataset is used for toxic comment classification?
The Jigsaw Toxic Comment Classification Challenge data on Kaggle. It has 159,571 labelled Wikipedia comments for training and 63,978 scored test comments, each marked with any of six labels: toxic, severe toxic, obscene, threat, insult and identity hate. It is released under a CC0 licence.
Is toxic comment classification multi-class or multi-label?
Multi-label. A single comment can be toxic, obscene and an insult at the same time, and most comments carry no label at all. So the model needs six independent yes or no outputs with a sigmoid on each, not one softmax across six classes.
Which model works best for toxic comment classification?
TF-IDF features with logistic regression make a strong and fast baseline. A fine-tuned transformer such as DistilBERT or BERT usually scores higher, at the price of training time. Train both, so you can show what the larger model adds.
Can I do this project in Hindi or Hinglish?
Yes, and it makes the project more original. Shared tasks such as HASOC publish hate and offensive speech data that includes Hindi and code-mixed text, and multilingual models such as MuRIL or XLM-R can be fine-tuned on it. Check each dataset’s terms before you use it.
Can The Ultimate Project World help with a toxic comment classification project?
Yes. Use the free consultation to tell us your level, your review dates and the model your guide prefers. We will tell you exactly which parts we can take on, such as the code, the demo, the report, the PPT and viva preparation.
