Feature-Specific sentiment analysis of iPhone reviews

This project focuses on analyzing customer reviews and textual data from an ecommerce platform to gain insights into customer experiences, product quality, and brand perception. By leveraging advanced machine learning, natural language processing (NLP), and lexicon-based approaches, we aim to extract actionable feedback that will guide product development. Moving beyond traditional sentiment classification (positive, negative, neutral), we implement feature-based sentiment analysis to identify specific aspects of the iPhone, such as battery life or camera quality, where customers express satisfaction or dissatisfaction. To achieve this, we employ techniques like term frequency-inverse document frequency (TF-IDF), part-of-speech tagging, and aspect-based sentiment analysis (ABSA), combined with lexicon-based approaches for sentiment scoring. Additionally, supervised learning models such as Support Vector Machines (SVM) and Random Forests, along with deep learning models like Recurrent Neural Networks (RNN) and BERT (Bidirectional Encoder Representations from Transformers), are used for sentiment classification. These approaches will provide nuanced insights into customer feedback, helping inform product refinement and future development strategies.