An Interactive Visualization System for a Multi-Level View of Opinion Mining Results

Abstract

Products are purchased and sold on a daily basis and people tend to critique on products they purchase. Those who want to buy a product will read reviews on that product given by others before buying; likewise those who have already bought a product will write a review on it. This paper presents a technique for visualizing data that comes from reviews given online for different products. My contribution to this project is to create a tool and process the tagged files generated with the help of machine learning. This project also focuses on the implementation of Semantic matching which reduces redundancy by grouping similar data together. Semantic matching helps put all the synonyms of the data together. Implementation of Semantic matching is supported by the implementation of error correction technique. Error correction improves data quality by correcting spelling mistakes made by people while writing reviews.