Patent Document ID: 8166032
Application ID: 12384880

Base Claim:
1. A computer implemented method of assessing human sentiment from a group of documents, each document in the group of documents having a plurality of terms and being digitally represented in a computer, the method comprising: receiving, by the computer, a group of documents, each document in the group of documents comprising a context of a plurality of terms and all documents in the group of documents representative of a particular topic; constructing, by the computer, a document sentiment vector space from the group of documents, wherein construction of the document sentiment vector space comprises: assessing sentimentality of each document in the group of documents toward the topic, by the computer, wherein sentimentality represents human emotion toward the topic, comprising: deriving a publication date for each document in the group of documents; electing an extrinsic metric for the particular topic for assessing the sentimentality toward the topic, the extrinsic metric being related to an affirmative and intentional human action with a value of the extrinsic metric being indicative of the human action; receiving extrinsic metric historical data for each document in the group of documents proximate to the respective publication date for each document; and examining the extrinsic metric historical data for each document proximate to the respective publication date for each document over a timeframe of influence for changes in the value of the extrinsic metric, wherein the timeframe of influence is a predetermined time period in which a context of a document influences humans to undertake an affirmative and intentional human action resulting in a change in the value of the extrinsic metric; identifying sentimentally significant documents in the group of documents with heightened sentimentality toward the particular topic, by the computer, comprising: receiving a sentiment value for the change in the extrinsic metric historical data, the sentiment value being indicative of sentimental significance; and comparing the sentiment value to the changes in the value of the extrinsic metric over the timeframe of influence for each document in the group; labeling the identified sentimentally significant documents, in the computer, by including a unique sentiment binding term in the context of the plurality of terms; representing, by the computer, each document in the group of documents in the document sentiment vector space; defining, by the computer, a region of sentimental significance in the document sentiment vector space based on an occurrence of document representations for the identified sentimentally significant documents with the unique sentiment binding term; receiving, by the computer, a query string; and assessing, by the computer, the sentimentality of the query string by comparing a representation of the query string for semantic similarity to the region of sentimental significance in the document sentiment vector space.

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Claim 11:
11. The method of claim 1 , wherein the query string is a document having a document publication date and comprises of a plurality of document terms and assessing the sentimentality of the a query string, by the computer, by comparing a representation of the query string for semantic similarity to the region of sentimental significance in the document sentiment vector space further comprises: creating a query term vector as the representation of the query string for the query string from a vector sum of query term vectors from a term-by-concepts matrix, for each of the plurality of terms co-occurring in the query string and the term-by concepts matrix; and comparing the query term vector for semantic similarity to the region of sentimental significance in the document sentiment vector space.