Sentiment Alignment, Amazon reviews against BERT
Star ratings and review text measure different things, and the gap is the finding
Most sentiment projects stop at classifying the review. This one runs a pre-trained multilingual BERT sentiment model across Amazon reviews, then compares what the model reads against the star rating the person actually gave, sorting the result into four alignment categories.
The gap is the finding. A three-star review often carries positive text: good product, not great. A five-star review is often flatly neutral: it works. Stars and text measure different things, and quantifying where they part company is more interesting than either number on its own.
To be precise about what this is: inference on a pre-trained model, not fine-tuning.
What this does not do
- This is inference on a pre-trained model, not training and not fine-tuning.