Description
This article explores a sentiment analysis technique used to analyze hidden emotions in product comments to improve product ratings. The system module has been modified to enhance sentimental analysis methodology for an online e-commerce application. Registered users can evaluate products, provide feedback, and contribute to the product’s ranking by analyzing user-provided comments for sentimental keywords. Positive and negative emotional keywords are used to assess user comments and improve product ratings.
Users can view comments from previous users, while the system administrator enters product details and sentimental keywords into the database. This system facilitates easy product search and serves as a platform for promoting products and marketing. The sentimental product rating system allows users to create product reviews, influencing consumer purchasing decisions based on ratings and reviews. The credibility of a product is established through favorable and negative comments, impacting consumer purchasing decisions.
The project “Sentiment Analysis Project on Product Rating” includes static pages such as a home page with an animated slider, an about us page, and a contact us page. The project utilizes HTML, CSS, JavaScript, Python, MySQL, Django, and Python libraries like numpy, nltk, pyparsing, and PySocks for development. It can be configured on Windows, Linux, and Mac operating systems.
