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.
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. 10. The method of claim 1 , wherein the query string is a plurality of terms and assessing the sentimentality of the query string by comparing, by the computer, the 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 occurring in the query string; and comparing the query term vector for semantic similarity to the region of sentimental significance in the document sentiment vector space.