1. A method of identifying electronic text messages as spam, the method comprising: (a) creating a hierarchic list of spam message categories and sub-categories, wherein the hierarchic list defines properties of key terms within the spam message categories and sub-categories; (b) composing a database of the key terms and a database of sample messages in a human language for each of the spam message categories and message templates for sub-categories, wherein the key terms are identified using human language-specific variants of a combination of separate words in a particular human language; (c) defining at least one spam message category from the hierarchic list of the spam message categories for which (i) a weight factor of a morphologically transformed text message exceeds a first pre-determined threshold or (ii) a similarity score of the text message exceeds a second pre-determined threshold, wherein the weight factor value and the similarity score value are compared against the respective threshold values using a precise matching comparison; and (d) associating with the at least one spam message category the text message having (i) the weight factor value exceeding the first threshold or (ii) the similarity score value exceeding the second threshold, wherein the properties of the key terms within the spam message categories are any of: a frequency of occurrence of the key term within the message; a location of the key term within the message; and a number of separate words in the key term.
1. A method of identifying electronic text messages as spam, the method comprising: (a) creating a hierarchic list of spam message categories and sub-categories, wherein the hierarchic list defines properties of key terms within the spam message categories and sub-categories; (b) composing a database of the key terms and a database of sample messages in a human language for each of the spam message categories and message templates for sub-categories, wherein the key terms are identified using human language-specific variants of a combination of separate words in a particular human language; (c) defining at least one spam message category from the hierarchic list of the spam message categories for which (i) a weight factor of a morphologically transformed text message exceeds a first pre-determined threshold or (ii) a similarity score of the text message exceeds a second pre-determined threshold, wherein the weight factor value and the similarity score value are compared against the respective threshold values using a precise matching comparison; and (d) associating with the at least one spam message category the text message having (i) the weight factor value exceeding the first threshold or (ii) the similarity score value exceeding the second threshold, wherein the properties of the key terms within the spam message categories are any of: a frequency of occurrence of the key term within the message; a location of the key term within the message; and a number of separate words in the key term. 2. The method of claim 1 , wherein the text messages are electronically acquired text documents or portions thereof.