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: The disclosed invention performs a set of operations on an email to analyze the text and generate a coherent summary. Email summaries are generated by applying a coherence layer after a ranking process. Analyzing how sentences relate to each other via discourse markers and other linguistic devices aids in enhancing coherence of the email summaries. Output summaries are more coherent and easier to understand because they mimic the flow of ideas contained in the original message instead of merely being a collection of extracted sentences. Tasks may also be extracted from the text of the email to assist users in keeping track of tasks that they receive via email.

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.