10. The method for managing and analyzing media contents recited in claim 8 , wherein said toning engine further weighs tones of authors of said at least one media content for establishing said most probable tone level of said each of said at least one media content, wherein said tones of said authors of said at least one media content are established by retrieving, from said at least one database, tone level probabilities for the author 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 authors.
8. A computer-implemented method for managing and analyzing at least one media content comprising the steps of: i. uploading said at least one media content from at least one content provider; 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, rationalizing nouns, pronouns and names therein, extracting attributes and categories of said each of said at least one media content, 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 and said extracted attributes and said categories of said each of said at least one media content, said one or more quotes, and said attributes of said one or more quotes into a plurality of tables in at least one database; and iv. retrieving data from said plurality of tables in said at least one database in response to a request from a client browser and generating a report and toning said at least one media content prior to said generating said report by carrying out the process comprising the 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, 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 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 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 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.