Sentiment Analysis of Beauty Product Reviews on the Female Daily Website

Sentiment analysis plays a critical role in understanding customer opinions from textual data, helping businesses and consumers make informed decisions. This project aims to develop an automated sentiment analysis system for beauty product reviews from the Female Daily website. The dataset consists of 34 features, including product brand, category, label, and review text. The goal is to classify each review as positive, negative, or neutral based on the content provided. The system utilizes machine learning models to accurately predict the sentiment of reviews, helping businesses gauge customer satisfaction and identify areas for improvement. The backend is implemented using Flask, while the front-end is built with HTML, CSS, and JavaScript, offering users an easy-to-use interface for submitting reviews and receiving sentiment predictions. This project automates the sentiment classification process, eliminating the need for manual analysis. It provides valuable insights for businesses looking to improve their products and services, while also helping consumers make better-informed purchasing decisions based on the collective sentiment of product reviews.

Keywords: Sentiment analysis, beauty product reviews, machine learning, classification, opinion mining, natural language processing, Flask framework, user interface, text classification, consumer insights.