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A System and a Method for Identifying Organizational Outliers from Personality Analysis of Text

IP.com Disclosure Number: IPCOM000244817D
Publication Date: 2016-Jan-19
Document File: 2 page(s) / 58K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method to enhance large-scale online collaboration software through a novel method of identifying individuals that are organizational outliers based on the textual analysis of real-time discussions between individuals.

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A System and a Method for Identifying Organizational Outliers from Personality Analysis of Text

Large-scale online collaboration software systems and methods consist of one or more of the following components:

• A high-profile online event • A defined agenda, focused on strategic and critical enterprise issues
• A real-time discussion database with ideas, best practices, and employee

  sentiment
• Real-time text mining and analysis to surface and steer live discussion trends

Presently, customers that commission large-scale online collaboration events are very interested in the identification of participants that provide unique perspectives and approaches to solving problems. Methods are needed to identify potential candidates that exhibit creative, fresh thinking based on an individual's contributions within the collaboration event.

Known solutions identify unique contributions (and hence contributors) to large-scale online collaboration events based on a variety of text analytics methods . These methods include ad-hoc approaches using document clustering, sentiment analysis, and thematic analysis of comments. Social network analysis may also be performed on the threaded discussions to identify relationships between participants .

The solution is to enhance large-scale online collaboration software through a novel method of identifying individuals that are organizational outliers based on the textual analysis of real-time discussions between individuals. The solution compares the personality profiles of individual contributors in a large-scale online collaboration event, based on the text of an individual's contributions, with the aggregate personality profile of the collaborating organization as a whole, based on all of the text contributed to the online collaboration event.

Current methods create personality profiles based on textual data using methods developed by research and corporate organizations. Known method can create aggregate personality profiles based on data from multiple individuals . However, it is not known to determine a "distance" or deviation of a personality profile from that of an aggregate personality profile. Methods are disclosed to discover the "distance" or deviation of an individual collaboration event participant personality profile from the collaborating organization's personality profile.

The proposed solution consists of multiple methods . Details for each method are disclosed below.

Generation of an individual online collaboration event participant personality profile
Known methods may be used to generate a personality profile based on an individual 's

written text. One such technology leverages advanced linguistic processing to infer

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from the user's writing the Big...