Text Analytics - Sentiment Classification on Amazon Product Reviews
NLP | Supervised Learning | Customer Feedback Analysis Tools & Techniques: Python, Pandas, Sklearn, NLTK, TF-IDF Vectorizer, Logistic Regression, Confusion Matrix, Classification Report 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 ...