1. A web-based media analysis computer system for analyzing at least one media content that includes text, comprising: i. a memory comprising at least one database being accessible to the computer system; ii. the computer system implementing an uploader that uploads said at least one media content from at least one content provider over a communication network; iii. the computer system implementing a parser that converts each of said at least one media content into serialized data, filters out unessential data from said serialized data according to a predefined list of unessential data, rationalizes nouns, pronouns and names in said each of said at least one media content, and extracts 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 from said serialized and filtered data of said each of said at least one media content using regular expressions and predefined parsing rules, relationally stores and cross-references said serialized and filtered data, 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 said at least one database, wherein said attributes of said one or more quotes comprise the name of the quoted person or organization; and iv. the computer system implementing an analysis module that retrieves data from said plurality of said tables in said at least one database and wherein said analysis module further comprises a toning engine that determines a tone level of said each of said at least one media, said toning engine is provided with tone level probabilities of meaningful minimum sections, tone level probabilities for attributes, and tone level probabilities for categories in said at least one database, said tone engine: a. parses each of said at least one media content and splitting into serialized meaningful minimum sections; b. retrieves, from said at least one database, said 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 determines 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. retrieves, from said at least one database, said extracted attributes of said each of said at least one media content and said tone level probabilities for each of said attributes of said each of said at least one media content, and determines most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes; d. retrieves, from said at least one database, said extracted categories of said each of said at least one media content and said tone level probabilities for each of said categories of said each of said at least one media content, and determines most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories; and establishes 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; and e. generates a report in response to a request from a client browser.
1. A web-based media analysis computer system for analyzing at least one media content that includes text, comprising: i. a memory comprising at least one database being accessible to the computer system; ii. the computer system implementing an uploader that uploads said at least one media content from at least one content provider over a communication network; iii. the computer system implementing a parser that converts each of said at least one media content into serialized data, filters out unessential data from said serialized data according to a predefined list of unessential data, rationalizes nouns, pronouns and names in said each of said at least one media content, and extracts 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 from said serialized and filtered data of said each of said at least one media content using regular expressions and predefined parsing rules, relationally stores and cross-references said serialized and filtered data, 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 said at least one database, wherein said attributes of said one or more quotes comprise the name of the quoted person or organization; and iv. the computer system implementing an analysis module that retrieves data from said plurality of said tables in said at least one database and wherein said analysis module further comprises a toning engine that determines a tone level of said each of said at least one media, said toning engine is provided with tone level probabilities of meaningful minimum sections, tone level probabilities for attributes, and tone level probabilities for categories in said at least one database, said tone engine: a. parses each of said at least one media content and splitting into serialized meaningful minimum sections; b. retrieves, from said at least one database, said 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 determines 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. retrieves, from said at least one database, said extracted attributes of said each of said at least one media content and said tone level probabilities for each of said attributes of said each of said at least one media content, and determines most probable tone levels of said each of said at least one media content based on said tone level probabilities of said attributes; d. retrieves, from said at least one database, said extracted categories of said each of said at least one media content and said tone level probabilities for each of said categories of said each of said at least one media content, and determines most probable tone levels of said each of said at least one media content based on said tone level probabilities of said categories; and establishes 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; and e. generates a report in response to a request from a client browser. 3. The web-based media analysis system recited in claim 1 , 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.