Abstract: A predictive modeling system and methodology makes predictions using unstructured content as an input, either alone or in conjunction with structured content. Content transformation rules are selected for application to the unstructured content, such as emails, call center notes, and other forms of human communication, by identifying the rules that are likely to improve the performance of a predictive modeling system.

Abstract: The present invention provides for a system and method of assigning attribute scores, including those for abstract and diverse concepts such as sentiment (e.g., happiness, anger, sadness) to documents containing unstructured content (natural language content). Such content may include, but is not limited to, Web pages, e-mails, word processing documents, computer logs, chat logs, audio files, graphical images, text files, books, magazines, articles, etc. The attributes that are scored are specialized views of the unstructured content. Accordingly, attribute scoring denotes the processes of assigning one or more new measures to a document containing unstructured content. The attribute score or measure indicates the degree of the attributes found within the document.

Abstract: The present invention provides for a system and method of assigning attribute scores, including those for abstract and diverse concepts such as sentiment (e.g., happiness, anger, sadness) to documents containing unstructured content (natural language content). Such content may include, but is not limited to, Web pages, e-mails, word processing documents, computer logs, chat logs, audio files, graphical images, text files, books, magazines, articles, etc. The attributes that are scored are specialized views of the unstructured content. Accordingly, attribute scoring denotes the processes of assigning one or more new measures to a document containing unstructured content. The attribute score or measure indicates the degree of the attributes found within the document.