Text Analytics - Sentiment Classification on Amazon Product Reviews
NLP | Supervised Learning | Customer Feedback Analysis
Overview:
In the world of e-commerce, reviews carry more weight than ads. Customer reviews are one of the most powerful tools that shape e-commerce decision-making. I wanted to understand how natural language processing could help businesses extract insight from that unstructured feedback. So, I built a sentiment classification model using real Amazon product reviews, turning raw text into structured, decision-ready insight, applying end-to-end text analytics and machine learning in Python
Approach:
-Explored a dataset of Amazon reviews labelled as positive or negative
-Preprocessed text (lowercasing, removing stop words/punctuation, stemming)
-Converted text to vectors using TF-IDF to capture important terms
-Trained a Logistic Regression model for sentiment classification
-Split data into train/test sets and evaluated using accuracy, precision,
and recall to ensure balanced predictions
Outcome:
- Achieved
88% accuracy in predicting sentiment on test data
- Strong
F1 scores across both classes
- Demonstrated
consistent performance in handling noisy, user-generated text
Business Application:
E-commerce platforms like Amazon can use this type of model
to:
- Automatically
flag negative reviews for product teams
- Power
dashboards for category managers (e.g. % positive reviews per SKU)
- Drive
personalisation by mapping sentiment trends to user preferences