24. The method of toning said at least one media content recited in claim 20 , wherein said tone level probabilities for all of said serialized meaningful minimum sections, said tone level probabilities for said attributes of said each of said at least one media content, and said tone level probabilities for said categories of said each of said at least one media content are determined through a training process by feeding a plurality of sample media contents with suggested tones thereof and updating said at least one database, wherein said training process comprises the steps of: i. splitting each of said plurality of said sample media contents into meaningful minimum sections while stripping out any unessential sections and extracting attributes and categories of said each of said plurality of said sample media contents using regular expressions; ii. tracking number of appearances of each of said meaningful minimum sections in said each of said plurality of said sample media contents and associating said number of appearances and said suggested tone corresponding to said each of said plurality of said sample media contents for deriving probabilities of said each of said meaningful minimum sections cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said meaningful minimum sections; iii. deriving said tone level probabilities of said attributes of said each of said plurality of said sample media contents, and updating said at least one database with said tone level probabilities of said attributes; and iv. deriving said tone level probabilities of said categories of said each of said plurality of said sample media contents, and updating said at least one database with said tone level probabilities of said categories.
24. The method of toning said at least one media content recited in claim 20 , wherein said tone level probabilities for all of said serialized meaningful minimum sections, said tone level probabilities for said attributes of said each of said at least one media content, and said tone level probabilities for said categories of said each of said at least one media content are determined through a training process by feeding a plurality of sample media contents with suggested tones thereof and updating said at least one database, wherein said training process comprises the steps of: i. splitting each of said plurality of said sample media contents into meaningful minimum sections while stripping out any unessential sections and extracting attributes and categories of said each of said plurality of said sample media contents using regular expressions; ii. tracking number of appearances of each of said meaningful minimum sections in said each of said plurality of said sample media contents and associating said number of appearances and said suggested tone corresponding to said each of said plurality of said sample media contents for deriving probabilities of said each of said meaningful minimum sections cause said each of said plurality of said sample media contents to be toned at, and updating said at least one database with said tone level probabilities of said meaningful minimum sections; iii. deriving said tone level probabilities of said attributes of said each of said plurality of said sample media contents, and updating said at least one database with said tone level probabilities of said attributes; and iv. deriving said tone level probabilities of said categories of said each of said plurality of said sample media contents, and updating said at least one database with said tone level probabilities of said categories. 25. The method of toning said at least one media content recited in claim 24 , wherein said meaningful minimum sections are words.