ReviewTrawler

About

ReviewTrawler Make Better Online Purchasing Decisions Overview Online retailers such as Amazon and NewEgg, or travel sites such as TripAdvisor or Expedia, have large datasets of reviews on products or services. These reviews contain key information in relation to features and their performance, which can be used by a person or business to inform a purchase. However, there is currently no easy way to corral this rich but myriad data from numerous reviews on a product or service, potentially sourced across several online companies, into a form where a meta-review can be generated which might eventually inform a purchase. How It Works A 3-step approach is carried out for a given product. Firstly, shallow NLP techniques extract candidate features from reviews of a product. Secondly, associated sentiment for each feature is evaluated. Finally, features and overall sentiment scores are aggregated to generate experiential product meta-reviews. Recommendations can be made based on; - Product Similarity (feature sets) - Sentiment (performance) - Combining Similarity with Sentiment (feature sets and performance) Benefits - Thousands of reviews for a product or service, sourced from multiple online sources, can be automatically aggregated to provide quick and powerful information - Manufacturers can inform product development using the information on feature sets and the sentiment regarding the feature sets - Potential buyers can use meta-reviews of a product to understand the product features as well as the features found in other products, as well as the sentiment in those features, to aid in purchasing decision Protection US Patent 61/827,054 filed on 24/05/2013 Inventors Barry Smyth Ruihai Dong Michael O'Mahony