This project aims to analyze product reviews using advanced natural language processing and data analytics techniques. The objective is to extract sentiment polarity and key themes from textual data to support product improvement and customer satisfaction analysis.
Collect large-scale product review datasets from e-commerce platforms.
Perform advanced text preprocessing including lemmatization and noise removal.
Conduct exploratory text analysis and n-gram analysis.
Apply feature extraction techniques such as TF-IDF and word embeddings.
Implement machine learning or deep learning-based sentiment classifiers.
Evaluate model performance using accuracy, precision, and confusion matrices.
Perform topic modeling to identify recurring customer concerns.
Visualize sentiment trends and themes.
Interpret insights for product strategy decisions.
Document model design, evaluation, and limitations thoroughly.
