System for Estimating a Distribution of Message Content Categories in Source Data
Daniel Hopkins, Gary King, Ying Lu. 2012.
"System for Estimating a Distribution of Message Content Categories in Source Data".
United States of America 8,180,717.

Abstract
A method of computerized content analysis that gives “approximately unbiased and statistically consistent estimates” of a distribution of elements of structured, unstructured, and partially structured soruce data among a set of categories. In one embodiment, this is done by analyzing a distribution of small set of individually-classified elements in a plurality of categories and then using the information determined from the analysis to extrapolate a distribution in a larger population set. This extrapolation is performed without constraining the distribution of the unlabeled elements to be euqal to the distribution of labeled elements, nor constraining a content distribution of content of elements in the labeled set (e.g., a distribution of words used by elements in the labeled set) to be equal to a content distribution of elements in the unlabeled set. Not being constrained in these ways allows the estimation techniques described herein to provide distinct advantages over conventional aggregation techniques.
See Also
- [Patent] System for Estimating a Distribution of Message Content Categories in Source Data (2nd) (2015)
- [Paper] Estimating Incidence Curves of Several Infections Using Symptom Surveillance Data (2011)
- [Presentation] Big Data Is Not About the Data! (2018)
- [Presentation] Big Data Reveals Made Up Data: How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument (2017)
- [Presentation] Big Data Is Not About the Data! The Power of Modern Analytics (2016)
- [Book] Preface: Big Data Is Not About the Data! (2016)
- [Presentation] Big Data Is Not About the Data, With Applications (2015)
- [Presentation] Big Data Is Not About The Data! (2013)