Customer Review Sentiment Classification Using Machine Learning

—Customer review sentiment classification is a
major application of Natural Language Processing used
to identify customer opinions, emotions, and satisfaction
levels from textual feedback. Modern businesses receive
large numbers of reviews through e-commerce websites,
food delivery applications, hotel booking portals, and
social media platforms. Manual analysis of such large
review datasets is difficult, slow, and inconsistent. This
project develops a Python-based machine learning
system to automatically classify customer reviews into
positive, negative, and neutral sentiments. Text
preprocessing methods such as tokenization, stop-word
removal, punctuation removal, lowercasing, stemming,
lemmatization, and TF-IDF vectorization are applied to
improve prediction accuracy. The Multinomial Naive
Bayes algorithm is used for classification because it is
simple, fast, memory-efficient, and highly effective for
text classification tasks. The system improves customer
satisfaction analysis, service quality, and business
decision-making.