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

The present invention provides improvements in processing published content for users to associate sentiment to content, cluster content for review, and extract core text. A method is provided for receiving an item of content, parsing the item of content with a microprocessor to generate a linguistic annotated item of content with language associations, retrieving from a term selection rules repository stored upon a memory at least a rule of a plurality of rules, applying the at least a rule of the plurality of rules to establish a set of candidate sentiment carrying terms within the linguistic annotated item of content, querying the set of candidate sentiment carrying terms against a target-domain sentiment lexicon to generate a set of sentiment labeled terms, and applying to the linguistic annotated item of content a set of sentiment labeling rules established in dependence of at least the set of sentiment labeled terms to generate a sentiment label for the item of content. Other aspects of the invention allow for identifying themes in content, selecting a core multi-item concept, and establishing a sentiment relating to the core multi-item concept for the plurality of items of content.

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 with a document's constituent terms is derived from the same sentiment context as the document.

Prior art techniques are also not arrived at by a rigorous linguistic analysis of the document.

It would also be evident that the prior art machine learning classification approaches can only operate on information that they have encountered before, i.e. only those features are supported that were included in the training document set's vocabulary.

Another limitation within prior art techniques is the ability to classify small documents, especially data sets derived from cellular SMS messages or Twitter status updates for example, as these documents are too small to accurately be classified by machine learning based sentiment classifiers.

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