Method and system relating to sentiment analysis of electronic content

Abstract

Users receive information which must be filtered, processed, analysed, reviewed, consolidated and distributed or acted upon. Prior art tools automatically processing content to assign sentiment to the content are ineffective as essential aspects such as context are not considered. Embodiments of the invention provide automatic contextual based sentiment classification of content in terms of both sentiments expressed and their intensity. Further a content set is analysed to rapidly establish an “at-a-glance” type assessment of the key topics/themes present within the content set and sentimentally annotate each. Importantly embodiments of the invention also provide for a user to establish the basis for the sentiment associated with an item of or set of content, i.e. make it explainable. Further embodiments of the invention provide for the establishment of psychological tone to sentiments where the sentiments and psychological tones to be tuned from the context or domain of the content.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This patent application claims the benefit of U.S. Provisional Patent Application 61 / 647,183 filed May 15, 2012 entitled “Method and System of Managing Content” the entire contents of which are incorporated by reference.FIELD OF THE INVENTION[0002]The present invention relates to published content and more specifically to the processing of published content for users to associate sentiment to the content.BACKGROUND OF THE INVENTION[0003]In 2008, Americans consumed information for approximately 1.3 trillion hours, or an average of almost 12 hours per day per person (Global Information Industry Center, University of California at San Diego, January 2010). Consumption totaled 3.6 zettabytes (3.6×1021 bytes) and 10,845 trillion (10,845×1012) words, corresponding to 100,500 words and 34 gigabytes for an average person on an average day. This information coming from over twenty different sources of information, from newspapers and books through...

AI Technical Summary

Benefits of technology

[0021]It is an object of the present invention to provide improvements in the art relating to published content and more specifically

Problems solved by technology

However, this analysis does not provide any context in respect of these occurrences with their context.

Most machine-learning based classification systems generate an opaque high-dimensional model such that the sentiment label associated with a document cannot be mapped back to the document, and thus there is no easily understandable method to describe how the class-association statistics associated with individual features are used to derive the sentiment label.

This “black-box” nature of the machine learning classifier can unnerve those who depend professionally on the veracity of the sentiment label to make business decisions.

Accordingly prior art machine learning based solutions do not ensure that the sentiment associated wi

Images