Predict Review Scores Using Text Embeddings

This prompt chain performs regression analysis using text embeddings to predict numerical review scores. It takes review text, converts it to embeddings, and trains a model to predict scores between 1 and 5 stars, where 1-star represents negative reviews and 5-star represents positive ones. The workflow achieves an average prediction error of 0.53 per score, roughly equivalent to predicting half of reviews perfectly and half off by one star.

Use this workflow when you need to predict numerical ratings from text reviews rather than categorical classifications. It is ideal for scenarios where you have labeled training data with numerical scores and want to evaluate model performance on unseen test data. Do NOT use this when you need categorical classification (predicting discrete labels rather than continuous numbers) or when you lack sufficient training data with numerical scores to split into training and testing sets.

The workflow requires a dataset split into training and testing sets, with review text and corresponding numerical scores between 1 and 5. The dataset is created in the Get_embeddings_from_dataset Notebook. Users provide review text that gets converted to embeddings. The output is a predicted numerical score for each review, along with performance metrics showing an average error of 0.53 per prediction.

This workflow is designed for data scientists and machine learning practitioners who need to predict numerical ratings from text data. It serves users who want to encode free text features within existing ML models or who need an alternative to training classifiers for label prediction. The approach is particularly valuable for teams working with review data who require continuous score predictions rather than binary or multi-class classifications.