14. A tangible computer readable medium storing executable computer program instructions which, when executed at a server, cause the server to perform a process for analyzing at least one media content, the process comprising the steps of: i. unloading said at least one media content from at least one content provider over a communication network; ii. converting each of said at least one media content into serialized data, filtering out unessential data from said serialized data according to a predefined list of unessential data, extracting attributes and categories of said each of said at least one media content, and extracting, one or more quotes and attributes of said one or more quotes in said each of said at least one media content using regular expressions, wherein said attributes of said one or more quotes comprise the name of the quoted person or organization; iii. relationally storing and cross-referencing said serialized and filtered data, said extracted attributes, said extracted categories and said one or more quotes into a plurality of tables in at least one database; iv. retrieving data from said plurality of said tables in said at least one database and generating a report in response to request from a user; and v. toning said at least one media content prior to said generating said report by carrying out the process steps of: a. parsing each of said at least one media content and splitting into serialized meaningful minimum sections; b. retrieving, from said at least one database, tone level probabilities for each of said serialized meaningful minimum sections that cause said each of said at least one media content to be toned at, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said serialized meaningful minimum sections; c. retrieving, from said at least one database, tone level probabilities for each of said attributes of said media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes; d. retrieving, from said at least one database, tone level probabilities for each of said categories of said each of said at least one media content, and determining most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories; and e. establishing said most probable tone level of said each of said at least one media content by weighing and ranking said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said each of said serialized meaningful minimum sections, said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes, and said most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories.
18. The computer readable medium recited in claim 14 , 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, *deriving said tone level probabilities of said each of said meaningful minimum sections, 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 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 attributes; and iv. deriving said tone level probabilities of said categories of said each of said plurality of said sample media contents 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 categories.