1. A computer-implemented method for building an analysis database associating each of a plurality of n-grams with corresponding respective cognitive motivation orientations, comprising: receiving a training corpus of training documents in electronic form; each training document comprising a plurality of meaningfully arranged words; each training document having at least one annotated word sequence therein; wherein within each training document, each particular annotated word sequence is annotated with a corresponding word-sequence-level annotation identifying at least one cognitive motivation orientation that is associated with that particular annotated word sequence; for each training document: for each annotated word sequence in that particular training document: extracting n-grams overlapping that particular annotated word sequence; and associating each extracted n-gram with the at least one cognitive motivation orientation associated with that particular annotated word sequence; generating a set of indicator candidate n-grams wherein: each indicator candidate n-gram represents all instances of a particular n-gram in the training corpus for which at least one instance of that particular n-gram was extracted from any annotated word sequence in any training document; each indicator candidate n-gram being associated with every cognitive motivation orientation that is associated with at least one instance of the particular n-gram represented by that particular indicator candidate n-gram; applying at least one relevance filter to each indicator candidate n-gram in the set of indicator candidate n-grams to obtain a set of indicator n-grams, wherein: the set of indicator n-grams is a subset of the set of indicator candidate n-grams, so that each indicator n-gram corresponds to only one indicator candidate n-gram and thereby each indicator n-gram represents all instances of a corresponding particular n-gram in the training corpus for which at least one instance of that particular n-gram was extracted from any annotated word sequence in any training document; each indicator n-gram is associated with only a single cognitive motivation orientation; and each indicator n-gram has, as its associated single cognitive motivation orientation, that single cognitive motivation orientation with which the instances of the particular n-gram represented by that particular indicator n-gram are most frequently associated; permitting the analysis database to support enhanced computer-implemented neurolinguistic analysis of a text sequence to identify cognitive motivation orientations expressed within the text sequence quickly, objectively and consistently.
1. A computer-implemented method for building an analysis database associating each of a plurality of n-grams with corresponding respective cognitive motivation orientations, comprising: receiving a training corpus of training documents in electronic form; each training document comprising a plurality of meaningfully arranged words; each training document having at least one annotated word sequence therein; wherein within each training document, each particular annotated word sequence is annotated with a corresponding word-sequence-level annotation identifying at least one cognitive motivation orientation that is associated with that particular annotated word sequence; for each training document: for each annotated word sequence in that particular training document: extracting n-grams overlapping that particular annotated word sequence; and associating each extracted n-gram with the at least one cognitive motivation orientation associated with that particular annotated word sequence; generating a set of indicator candidate n-grams wherein: each indicator candidate n-gram represents all instances of a particular n-gram in the training corpus for which at least one instance of that particular n-gram was extracted from any annotated word sequence in any training document; each indicator candidate n-gram being associated with every cognitive motivation orientation that is associated with at least one instance of the particular n-gram represented by that particular indicator candidate n-gram; applying at least one relevance filter to each indicator candidate n-gram in the set of indicator candidate n-grams to obtain a set of indicator n-grams, wherein: the set of indicator n-grams is a subset of the set of indicator candidate n-grams, so that each indicator n-gram corresponds to only one indicator candidate n-gram and thereby each indicator n-gram represents all instances of a corresponding particular n-gram in the training corpus for which at least one instance of that particular n-gram was extracted from any annotated word sequence in any training document; each indicator n-gram is associated with only a single cognitive motivation orientation; and each indicator n-gram has, as its associated single cognitive motivation orientation, that single cognitive motivation orientation with which the instances of the particular n-gram represented by that particular indicator n-gram are most frequently associated; permitting the analysis database to support enhanced computer-implemented neurolinguistic analysis of a text sequence to identify cognitive motivation orientations expressed within the text sequence quickly, objectively and consistently. 2. The method of claim 1 , wherein applying at least one relevance filter to each indicator candidate n-gram in the set of indicator candidate n-grams to obtain a set of indicator n-grams comprises: excluding from the set of indicator n-grams those indicator candidate n-grams for which there is no single cognitive motivation orientation with which instances of the particular n-gram represented by that particular indicator candidate n-gram are most frequently associated in the training corpus; and further comprises at least one of: (a) excluding from the set of indicator n-grams those indicator candidate n-grams for which a predetermined minimum multiple condition is not satisfied, wherein the predetermined minimum multiple is condition satisfied in respect of a particular indictor candidate n-gram only if: (A) there is a single cognitive motivation orientation with which instances of the particular n-gram represented by that particular indicator candidate n-gram are most frequently associated in the training corpus, wherein instances of the particular n-gram represented by that particular indicator candidate n-gram appear p times in the training corpus in association with the single cognitive motivation orientation with which instances of the particular n-gram represented by that particular indicator candidate n-gram are most frequently associated in the training corpus; (B) there is at least one cognitive motivation orientation with which instances of the particular n-gram represented by that particular indicator candidate n-gram are second-most frequently associated in the training corpus, wherein instances of the particular n-gram represented by that particular indicator candidate n-gram appear q times in the training corpus in association with the at least one cognitive motivation orientation with which instances of the particular n-gram represented by that particular indicator candidate n-gram are second-most frequently associated in the training corpus; and (C) p exceeds the value resulting from multiplying q by a predetermined multiplier value r; (b) excluding from the set of indicator n-grams those indicator candidate n-grams for which p is less than a predetermined minimum number; and (c) excluding from the set of indicator n-grams those indicator candidate n-grams for which a calculated percentage, the calculated percentage being the value obtained by: dividing p by a total number of appearances in the training corpus of instances of the particular n-gram represented by the particular indicator candidate n-gram; and multiplying the resulting quotient by 100.