NLP Pipeline Turns Apple Tweets Into Actionable Business Intelligence

A supervised machine learning project uses the Apple Twitter Sentiment dataset to automatically classify tweets as positive, neutral, or negative. Raw tweets are preprocessed through tokenization, stop word removal, and lemmatization before being converted into numerical vectors using TF-IDF weighting. Multiple classifiers — including Logistic Regression, Naive Bayes, SVM, and Random Forest — are trained and compared across metrics such as F1-score and ROC-AUC to identify the best performer. The top model is then fine-tuned and deployed to classify new tweets in real time, routing outputs to relevant teams based on detected sentiment and topic. The pipeline aims to help businesses convert unstructured social media data into early brand warnings, product feedback, and targeted marketing decisions.