Abstract: A system, method, and computer program product for researching online reviews to assess the performance and functionality of digital media consumer products bought online or not (e.g. eBooks, movies, TV shows, music, DVD's, etc.). The system extracts reviews from multiple online sources, including online “stores”, professional articles, blogs, online magazines, websites, etc.; and, utilizes sentiment analysis algorithms and supervised machine learning analysis to present more informative summaries for each product's reviews, wherein each summary includes a sentence that encapsulates a sentiment held by many users; the most positive and negative comments; and a list of features with average scores (e.g. performance, price, etc.). Additionally, the user may view a separate review detail page per product that provides further summaries, such as a short list of other products that the same reviewer gave a very positive review for the features. The user is then able to purchase the product via a link.

Abstract: Systems and methods for summarizing online articles for consumption on a user device are disclosed herein. The system extracts the main body of an article's text from the HTML code of an online article. The system may then classify the extracted article into one of several different categories and removes duplicate articles. The system breaks down the article into its component sentences, and each sentence is classified into one of three categories: (1) potential candidate sentences that may be included in the generated summary; (2) weakly rejected sentences that will not be included in the summary but may be used to generate the summary; and (3) strongly rejected sentences that are not included in the summary. Finally, the system applies a document summarizer to generate quickly readable article summaries, for viewing on the user device, using relevant sentences from the article while maintaining the coherence of the article.

Abstract: A system, method, and computer program product (e.g. mobile App) and/or web-based service is provided to enable users to research online reviews in order to assess the performance and functionality of mobile applications. The system extracts reviews from multiple online sources, including: mobile Apps “stores”, blogs, online magazines, websites, etc.; and, utilizes sentiment analysis algorithms and supervised machine learning analysis to present more informative summaries for each App's reviews. Summaries may include: a sentence that encapsulates a sentiment held by many users; the most positive and negative comments; and a list of features with average scores (e.g. graphics, fun, easy to use, etc.). Additionally, the user may view a separate review detail page per App that provides further summaries, such as a short list of other Apps that the same reviewer gave a very positive review for the features. The user is then able to purchase and download the App via a link.