An NLP system that detects humor in code-mixed Telugu-English text.
A custom dataset of 5,000 romanized Telugu-English code-mixed jokes is curated and used to train a humor classification model, augmented with supplementary English joke datasets and evaluated on accuracy, precision, recall and F1-score.
Almost all humor detection research assumes clean, monolingual English. Indian social media is overwhelmingly code-mixed and romanized, which breaks both tokenizers and pretrained embeddings.
Build the dataset that does not exist, then train and evaluate humor classification directly on romanized Telugu-English rather than translating it into English first.
Custom Dataset Curation
5,000 romanized Telugu-English code-mixed jokes.
Humor Classification Model
Detects humorous vs non-humorous text.
Cross-Lingual Dataset Augmentation
Supplementary English joke datasets for training.
Performance Evaluation
Accuracy, precision, recall, and F1-score reporting.
NLP
Machine Learning
Data
Dataset curation
Romanized Telugu-English jokes are collected and labelled.
Preprocessing
Code-mixed text is normalised and tokenised.
Augmentation
English joke corpora supplement the training data.
Classification
The model predicts humorous vs non-humorous.
Evaluation
Results are reported across standard metrics.
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Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.
That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.
We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.
Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.
Get complete source code, documentation, PPT, explanation, and support.
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