Sentiment Analysis and a Customer Review Lab
Explore how to perform sentiment analysis on customer reviews by preparing text data, applying TF-IDF vectorization, training a logistic regression model, and interpreting results. Learn to evaluate model accuracy and use keyword summaries to identify review themes that inform product decisions.
A product team can monitor a growing review stream only when several pieces work together. Each incoming review needs consistent text preparation, a numeric TF-IDF representation, a trained classifier, and a summary that converts hundreds of predictions into issues worth reviewing. No single piece is sufficient. A keyword list cannot reliably assign sentiment, while a sentiment label alone cannot tell the product manager whether negative reviews center on delivery, billing, or battery life.
Sentiment analysis predicts a label from text. In this lab, one review is one prediction unit, and the output is a predicted positive or negative label. The model is a lightweight triage tool. It can help prioritize review reading and track changing patterns, but it will make mistakes and cannot determine why customers feel a certain way.
The pipeline carries one review through a visible sequence:
Raw review text such as
The battery dies before lunchTF-IDF features that record which terms matter in the training corpus
A probability that the review belongs to the positive class
A label chosen from that probability
A ranked term list for reviews assigned to each label
Train and check the sentiment pipeline
The first practical check is whether labeled examples support predictions on reviews the classifier did not train on. The inline dataset contains short product reviews ...