Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
Document Type
Event
Start Date
19-12-2024 4:00 PM
Description
The "Integrated Sentiment and Behavioral Analysis of Online Product Reviews" project helps businesses gain actionable insights from Product reviews by combining sentiment and behavioral analysis using NLP models like VADER and BERT. This dual approach categorizes reviews as positive, neutral, or negative and identifies themes such as preferences and complaints through Named Entity Recognition and topic modeling. By capturing both the emotional tone and specific product feedback, this method highlights consumer likes and pain points, assisting in targeted improvements for product design and customer service. The project addresses challenges in analyzing complex expressions like sarcasm, providing a robust framework for extracting meaningful insights from vast amounts of review data. Adaptable across various datasets, the model offers scalable benefits for enhancing e-commerce strategies through data-driven decisions based on real consumer feedback.
Included in
GMR-196 Integrated Sentiment and Behavioral Analysis of Online Product Reviews
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
The "Integrated Sentiment and Behavioral Analysis of Online Product Reviews" project helps businesses gain actionable insights from Product reviews by combining sentiment and behavioral analysis using NLP models like VADER and BERT. This dual approach categorizes reviews as positive, neutral, or negative and identifies themes such as preferences and complaints through Named Entity Recognition and topic modeling. By capturing both the emotional tone and specific product feedback, this method highlights consumer likes and pain points, assisting in targeted improvements for product design and customer service. The project addresses challenges in analyzing complex expressions like sarcasm, providing a robust framework for extracting meaningful insights from vast amounts of review data. Adaptable across various datasets, the model offers scalable benefits for enhancing e-commerce strategies through data-driven decisions based on real consumer feedback.