Natural Language Processing for Regional Language Sentiment Analysis

This research project addresses sentiment analysis for a low-resource regional Indian language, where labeled datasets and pretrained NLP models are far scarcer than for English. A dataset of social-media/product-review text is manually collected and annotated with positive/negative/neutral labels.

Several modeling approaches are compared: classical machine learning (TF-IDF + SVM/Naive Bayes), a fine-tuned multilingual transformer (mBERT/XLM-RoBERTa), and back-translation-based data augmentation to partially address the small dataset size.

The study reports F1-scores across the compared approaches, performs an error analysis highlighting challenges specific to the language (code-mixing with English, informal spelling variations), and discusses practical recommendations for building sentiment tools for other under-resourced languages.

Detailed Project Description

Natural Language Processing for Regional Language Sentiment Analysis applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- Python
- NLP
- Transformers
- scikit-learn
- mBERT

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Python, Hugging Face Transformers, Jupyter Notebook) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface