18. The method according to claim 1 further comprising using said text classifier most recently built to generate, for at least one individual issue in said set of issues, a set of keywords differentiating documents relevant to said individual issue to documents irrelevant to said individual issue.
1. An electronic document analysis method receiving N electronic documents pertaining to a case encompassing a set of issues including at least one issue and establishing relevance of at least the N electronic documents to at least one individual issue in the set of issues, the method performed with a processor, the method comprising, for at least one individual issue from among said set of issues: i. receiving an output of a categorization process applied to documents in at least control subsets of said at least N electronic documents, said output including, for each document in said subsets, one of a relevant-to-said-individual issue indication and a non-relevant-to-said-individual issue indication; ii. seeking an input as to whether or not to initiate a new iteration I; if a new iteration is initiated, perform steps iii-x; and if a new iteration is not initiated, go to step xi; iii. selecting m electronic documents from among a subset of the N electronic documents that are not in the control set and that were not used in previous rounds for training the classifier; iv. receiving an output of a categorization process applied to the m electronic documents; v. adding the m electronic documents to an existing training subset and building a text classifier simulating said categorization process using said output for all documents in said training subset of documents; vi. evaluating said text classifier's quality using said output for documents in said control subset; vii. selecting a cut-off point for binarizing said rankings of said documents in said control subset; viii. using said cut-off point, computing and storing at least one quality criterion characterizing said binarizing of said rankings of said documents in said control subset, thereby to define a quality of performance indication of a current iteration I; ix. displaying a comparison of the quality of performance indication of the current iteration I to quality of performance indications of previous iterations; x. returning to step ii; and xi. generating a computer display of said output of said categorization process received in step i as most recently performed, including, for each document in said subsets, one of a relevant-to-said-individual issue indication and a non-relevant-to-said-individual issue indication.