Predict Review Scores Using Text Embeddings

This prompt chain trains a regression model to predict numerical review scores from text embeddings. Instead of classifying reviews into categories, it predicts a continuous score between 1 and 5 stars, where 1-star represents negative sentiment and 5-star represents positive sentiment. The workflow splits data into training and testing sets to evaluate performance on unseen reviews, achieving 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 or scores from text data, particularly for review systems, sentiment analysis with granular scoring, or any scenario where you need continuous numerical predictions rather than discrete categories. It's ideal when you have labeled training data with numerical scores and want to leverage embeddings to capture semantic meaning in text.

Do NOT use this approach when you only need binary or multi-class classification (positive/negative/neutral). If you need to assign reviews to discrete categories rather than predict numerical values, train a classifier instead. Also avoid this method if you don't have sufficient labeled numerical training data to establish the score-to-embedding relationship.

You provide a dataset of reviews with associated numerical scores (1-5 scale) and their corresponding text embeddings. The embeddings must be generated beforehand using the Get_embeddings_from_dataset workflow referenced in the source material.

You receive a trained regression model capable of predicting review scores from new text embeddings, along with performance metrics showing an average error of 0.53 per prediction on the test set.

This workflow serves those building systems to predict review scores from text embeddings. The approach can also be adapted to train a classifier for label prediction or to incorporate embeddings as free text features within existing machine learning models.