Explainable Aspect-Based Sentiment Analysis on Review-Based Datasets

An explainable aspect-based sentiment analysis system for review datasets.

An explainable aspect-based sentiment analysis system for review datasets. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are explanations of each result (feature importance or visual evidence), text or speech data preparation, model training, fine-tuning or prompting, evaluation with task-appropriate metrics, and a chat or web interface to use it. The reference build uses SHAP, Python, Hugging Face Transformers, spaCy, and PyTorch, with a clear project structure, setup instructions and sample data so that it can be run and extended.