Abstract:
A method, computer program product and system are disclosed for determining the semantic density of textualized digital media (a measure of how much information is conveyed in a sentence or clause relative to its length). The more semantically dense text is, the more information it conveys in a given space. Users input a topic, a timeline, and one or more target web media sources for analysis. Text in the target media sources is deconstructed to determine density, and a density rating assigned to the web media source. Over time, users can track trends in the density of text media relative to a given topic, and determine how much information is being conveyed in connection with the topic, such as a political campaign. Line graphs, pie charts, and other time-elapsed output graphic representations of the semantic density are generated and rendered for the user.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to information retrieval systems, and more particularly relates to a computer implemented method and system for determining the information density of a target body of digital text. 
     2. Description of the Related Art 
     With the emergence of the Internet has come access to almost limitless amounts of information. Users typically seek access to this information with computer in logical communication with an information-retrieval system like Google® or other search engines. These search engines typically comprise a graphical user interface for accepting keywords used to identify relevant information, typically in the form of online electronic documents. 
     Typical search engines rank the importance of a document by the number of instances that the keyword appears in the body of text forming the document, the number of backlinks to the document, and the importance of the documents comprising the backlinks. This is problematic because many documents included in a given set of search results contain only superficial references to the keywords, and users are left to manually filter through irrelevant results in search for the documents conveying more denser amounts of information. 
     There exists no efficient means in the art of valuing the context in which keywords are appearing, or of determining the information density of sentences and clauses comprising the keywords and it synonyms. Methods, computer program products and systems are lacking in the art which determining the semantic density of textualized digital media. Semantic density is a measure of how much information is conveyed in a sentence or clause relative to its length in a given block of text. The more semantically dense text is, the more information it conveys in a given space. 
     The semantic density of text with regard to a particular topic is a useful metric to users desiring to measure trends in online chatter, news, such as a political campaign. 
     The present invention beneficially teaches a unique computer implemented methodology for determining the semantic word density of bodies of text in digitized documents which overcomes prior shortcomings in art. 
     SUMMARY OF THE INVENTION 
     From the foregoing discussion, it should be apparent that a need exists for a method, system, and computer program product for determining the semantic word density of a body of text. The present invention has been developed in response to the present state of the art; and, in particular, in response to the problems and needs in the art that have not yet been fully solved by currently available methods, systems, computer program products and apparatii, which invention overcomes many or all of the above-discussed shortcomings in the art. Accordingly, the present invention has been developed to provide a method, computer program product, and system for determining semantic word density. 
     A computer program product is disclosed, comprising a computer readable medium, having computer usable program code executable to perform operations for determining a semantic density of textualized digital web media, the operations of the computer program product comprising: compiling a list of web media sources for analysis in computer readable memory; querying the web media sources for a block of text; storing the block of text in volatile computer readable memory; identifying sentences within the block of text; storing the sentences as strings within an array; scanning each sentence to quantify one or more of the following: a number of words in the sentence; a number of prepositions, postpositions, adjectives, adverbs, verbs, nouns, and grammatical conjunctions, by referencing words within the sentence with a dictionary in computer readable memory; a number of dependent clauses in the sentence; a number of independent clauses in the sentence; a number of ellipsis, a number of dashes (both en dashes and em dashes), and a number of commas, semicolons, and colons; a number of subjects and predicates in the sentence; a number of appositions in the sentence; a number of syllables in each word of the sentence by cross-referencing each word with the dictionary in persistent storage; and a number of alphanumeric characters in the sentence; storing each quantified number in a persistent computer readable database with a time-stamp identifying the date the number(s) were quantified; calculating a semantic density score for each web media source in the list of web media sources, wherein the semantic density score is a function of the quantified numbers for each sentence in the web media source; and storing the semantic density score in a persistent computer readable database, the score exclusively associated with the web media content from which the score was derived. 
     In some embodiments, the computer program product of further comprises: prompting a user with a webpage to input a topic for semantic density determination; prompting the user with a webpage to input a timeline for semantic density determination; prompting the user with a webpage to input URLs for one or more web media sources; determining synonym(s) for the topic by referencing a thesaurus in computer readable memory; querying textualized media on the web media source for instances of the topic; querying textualized media on the web media source for instances of synonyms of the topic identified by referencing the dictionary; storing semantic density ratings for sentences comprising the topic; storing semantic density ratings for sentences comprising the synonyms of the topic; and rendering a graph on a computer display showing elapsed time across one axis, the graph showing a plurality of semantic density ratings for the web media identified by the user. 
     The web media sources may be queried at predetermined intervals of time. 
     The list of web media sources may consist of web media sources manually identified by one or more users to the website. The sentences may be identified within the block of text as being a string of text preceding a period immediately followed by a space. 
     Therein sentences may be identified within the block of text as being a string of text following a period (.) immediately followed by a space ( ) which string of text also precedes a period (.) immediately followed by a space ( ). 
     The computer program product may further comprise storing a dictionary in computer readable memory, the dictionary identifying words of a language, the dictionary characterizing each word as one or more of a noun, verb, adverb, adjective, and preposition, the dictionary identifying a number of syllables in each word. 
     In some embodiments, a dependent clause in the sentence may be identified as a string of text not comprising a verb within two commas. An independent clause in the sentence may be identified as a string of text comprising a verb within two commas. The semantic density score may further be a function of the number of sentences appearing in the block of text. A database query may be generated using the topic and synonyms, wherein the sentences are stored in a database in computer readable memory and queried with the database query by a DBMS. 
     The computer program product may further comprise rendering a graph on a computer display showing elapsed time across one axis, the graph showing a plurality of semantic word density ratings for the web media identified by the user. 
     A system to perform operations for relaying commercial offerings from select merchants to select consumers is also disclosed, the system comprising: a webserver; a semantic indexing server; an RDBMS; a topic prompter module configured to prompt a user with a graphic user interface to input a topic for semantic density determination; a timeline prompter module configured to prompt the user with a graphic user interface to input a timeline for semantic density determination; a URL prompter module configured to prompt the user with a graphic user interface to input URLs for one or more web media sources, the URLs compiled into a list of web media sources for analysis in computer readable memory; a synonym determiner module configured to determine synonym(s) for the topic by referencing a thesaurus in computer readable memory; a block storer module configured to store the blocks of text forming the web media sources in volatile computer readable memory; and a sentence identifier module configured to identify sentences within the block of text. 
     The system further comprises a sentence parser module configured to parse each sentence to quantify one or more of the following: a number of words in the sentence; a number of prepositions, postpositions, adjectives, adverbs, verbs, nouns, and grammatical conjunctions, by referencing words within the sentence with a dictionary in computer readable memory; a number of dependent clauses in the sentence; a number of independent clauses in the sentence; a number of ellipsis, a number of dashes (both en dashes and em dashes), and a number of commas, semicolons, and colons; a number of subjects and predicates in the sentence; a number of appositions in the sentence; a number of syllables in each word of the sentence by cross-referencing each word with the dictionary in persistent storage; and a number of alphanumeric characters in the sentence. 
     The system further comprises a value storer module configured to store each quantified number in a persistent computer readable database with a time-stamp identifying the date the number(s) were quantified; a calculator module configured to calculate a semantic density score for each web media source in the list of web media sources, wherein the semantic density score is a function of the quantified numbers for each sentence in the web media source; a density storer module configured to store the semantic density score in a persistent computer readable database, the score exclusively associated with the web media content from which the score was derived; a text querier module configured to query textualized media on the web media source for instances of the topic; a synonym query module configured to query textualized media on the web media source for instances of synonyms of the topic identified by referencing the dictionary; and a render module configured to render a graph on a computer display showing elapsed time across one axis, the graph showing a plurality of semantic density ratings for the web media identified by the user. 
     A second computer program product is disclosed comprising a computer readable medium having computer usable program code executable to perform operations for determining a semantic density of textualized digital web media, the operations of the computer program product comprising: prompting a user with a graphic user interface to input one or more keyword(s) for semantic density determination; prompting the user with the graphic user interface to input a timeline for semantic density determination; prompting the user with the graphic user interface to input domain names for one or more web media sources, the domain names compiled into a list of web media sources for analysis in computer readable memory; storing the blocks of text in the web media sources in volatile computer readable memory; and identifying sentences within the block of text, wherein sentences are identified within the block of text as being a string of text following a period (.) immediately followed by a space ( ) which string of text also precedes a period (.) immediately followed by a space ( ). 
     The computer program product further comprises storing the sentences as strings within an array; identifying clauses within the sentences satisfying one or more of the criteria from the group consisting of: a string of text enclosed ellipsis; a string of text enclosed by en dashes a string of text enclosed by em dashes; a string of text within the sentence enclosed by one or more of commas, semicolons, and colons; storing the identified clauses as strings within an array; calculating a semantic density score for each web media source in the list of web media sources, wherein the semantic density score is a function of the number of clauses in each sentence of the web media source; storing the semantic density score in a persistent computer readable database, the score exclusively associated with the web media content from which the score was derived; determining synonym(s) for the keyword by referencing a thesaurus in computer readable memory; querying the sentences for instances of the keyword and synonyms; storing semantic density ratings for sentences comprising the keyword and synonyms; rendering a graph on a computer display showing elapsed time across one axis, the graph showing a plurality of semantic density ratings for the web media identified by the user. 
     Reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present invention should be or are in any single embodiment of the invention. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present invention. Thus, discussion of the features and advantages, and similar language, throughout this specification may, but do not necessarily, refer to the same embodiment. 
     Furthermore, the described features, advantages, and characteristics of the invention may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize that the invention may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the invention. 
     These features and advantages of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth hereinafter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order that the advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which: 
         FIG. 1  is a UML deployment diagram illustrating one embodiment of a system for determining semantic word density in accordance with the present invention; 
         FIG. 2  is a data-entity relationship diagram illustrating the interrelationships and dataflow of the operations and modules of the present invention; 
         FIG. 3  is a flowchart illustrating method steps of the present invention, including steps which may be performed determining a semantic density of textualized digital web media in accordance with the present invention; 
         FIG. 4  is a block diagram illustrating one embodiment of a system which determines a semantic density of textualized digital web media in accordance with the present invention; 
         FIG. 5  is sentence content unit subdivided into clauses in accordance with the present invention; 
         FIG. 6  is a Booch-relationship diagram illustrating the relationships between the search engine, users, and web media pages in accordance with the present invention; 
         FIG. 7  is a flowchart illustrating method steps of the present invention, including steps which may be performed determining a semantic density of textualized digital web media in accordance with the present invention; 
         FIG. 8A  is a line graph illustrated a time elapsed display of semantic density ratings for a general topic in accordance with the present invention; and 
         FIG. 8B  is a line graph illustrated a time elapsed display of semantic density ratings for a specific topic in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment. 
     The described features, structures, or characteristics of the invention may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the invention. The apparatus modules recited in the claims may be configured to impart the recited functionality to the apparatus. 
       FIG. 1  is a UML deployment diagram illustrating one embodiment of a system  100  for determining semantic word density in accordance with the present invention. The system  100  comprises a web server  102 , a DBMS  104 , persistent storage  106 , keywords  108 , web content  110 , a user  112   a , a user  112   b , a network  114 , a server  116   a , a server  116   b , media content  118   a , media content  118   b , and an indexing server  120 . 
     The web server  102  may also comprise a computer program running on one or more data processing devices (DPDs), such as a server, computer workstation, router, mainframe computer, or the like. In various embodiments, the DPD comprises one or more processors. The processor is a computing device well-known to those in the art and may include an application-specific integrated circuit (“ASIC”). 
     A legal entity controls the web sever  102 . This legal entity may comprise any individual, organization, entity, or combination thereof, tasked with administering the server  102 . The legal entity may be a legal entity, such as a partnership, limited liability company (LLC), corporation, joint venture, municipality, d/b/a, or the like. Information may be routed from the sever  102  and/or from the indexing server  120  to any DPD, device, person, merchant, consumer, company, organization, or the like, in logical communication with the server  102 . 
     The server  102  communicates via the Internet  114  with the DPDs of users  112   a - b  through PCs, smart phones, or other personal DPDs such as laptops, Palm Pilots®, digital cameras, and the like. The server  102  may be in logical communication with users  112   a - b  through a networked environment such as the Internet  114 . The Internet  114  may comprise a local area network (LAN) or wide area network (WAN). The server  102  may email search results to users  112   a - b  using variations of the Simple Mail Transfer Protocol (SMTP), Internet Message Access Protocol (IMAP), Post Office Protocol (POP), or other protocols well-known to those of skill in the art. 
     The web server  102 , in the shown embodiment, comprises a database management system (DBMS)  104  or relational database management system (RDBMS), such as Oracle, MySQL, SQL, FireBird, IBM DB2®, or the like. 
     In the shown embodiment, one or more users  112  login to an account they have created with the server  102 , wherein the account information is stored in computer readable database memory  106  accessible by the server  102 . In other embodiments, it is not necessary for the users  112   a - b  to have accounts with the server  102 . 
     The users  112   a - b  may comprise any individual, organization, or group of individuals interested in the trends of semantic word density of a general or specific topic over a period of time. The users  112   a - b  access the web server  102  via the Internet  114 , interfacing with the server  102  using a graphic user interface on a DPD local to the users  112   a - b  (this graphic user interface usually comprising a website shown in an Internet browser on a computer display). 
     The servers  116   a - b , in the shown embodiment, comprises DPDs hosting third-party website with digital media content  118   a - b , which content  118   a - b  is accessible via the Internet or another network by the server  102  and/or the server  120 . 
     The users  112   a - b  submit topics for processing to the sever  102 . These 
     The indexing server  120  scans textualized web media content for text blocks, and parses text found on the web media sources into sentences, which sentences are arrayed in persistent storage  106 . Web media content  118   a - b  gathered from web media sources on the servers  116   a - b  is transferred to the server  102  and then to the indexing server  120 , or alternatively straight to the indexing server  120 . 
     In various embodiments of the present invention, the indexing server  120  is configured to identify and store in volatile or nonvolatile memory  106  copies of text on web media sources identified by the users  112   a - b ; or pointers or references to those web media sources on servers  116   a - b . In various embodiments of the present invention, the sever  102  and/or the indexing server  120  may be configured to scan a network  114  and identify and store in volatile or nonvolatile memory  106  copies of text found on a network, even at sources not identified ahead of time by the users  112   a - b.    
     In some embodiments, this scan is done before a topic for analysis is submitted by the users  112   a - b  to the server  102 . In other embodiments, the scan is done subsequently and only web media sources found to contain references to the topic (or its synonyms) are parsed and stored by the indexing server  120  in memory  106 . 
     In some embodiments, the display may be a cathode ray tube (“CRT”), a liquid crystal display (“LCD”), a plasma display panel (“PDP”), or the like. 
       FIG. 2  is a data-entity relationship  200  diagram illustrating the interrelationships and dataflow of the operations and modules of the present invention. The relationship  200  comprises users  112   a - c , web media sources  118   a - b , and storage  106 . The storage  106  comprises computer readable files, including member structure  216 , web content  110 , density rating  218 , keywords  108 , and AST graphs  220 . Various species of data are transmitted back and forth between the users  112   a - c , the web media  118   a - b , and storage  106 , including keywords  108   a - b , timelines  202   a - b , URL (Uniform Resource Locators)  204   a - c , sentences  210   a - z , clauses  212   a - z , statistics  214   a - g , density data  216 , and time elapsed semantic word graphs  218 . 
     In the shown embodiment, user  112   a  is prompted to upload via a graphic user interface a number of pieces on information to the server  102 , including one or more keyword(s)  108   a , a timeline  202   a  with a start date (i.e. start point) and an end date, and plurality of URLs  204   a - b.    
     The keyword(s)  108  connote topics of interest to the users  112   a  for which the user  112   a  would like semantic word density information in the form of graphs  218  (shown further in  FIGS. 8A-8B  below) or statistics  214 . 
     The keyword(s)  108  are stored in a database readable file  108  in storage  106 . 
     A plurality of URLs to web media content  118   a - b  are identified by the user  112   a  which the user  112   a  would like searched for textualized media. The URLs  204   a - b  are stored in storage  106 , and the indexing server  120  scans the web media sources  118   a - b  identified by the URLs  204   a - b  for textualized web media content. This textualized web media content is parsed into sentences  210   a - z  and clauses  214   a - f , which sentences  210   a - z  and clauses  214   a - f  are timestamped and stored in storage  106 . Dependencies between the clauses  212   a - z  are determined as further described below in relation to  FIG. 4  and other subsequent Figures. These dependencies are used to create statistics describing the magnitude of word density contained by each sentence in the respective web media  118  (i.e. density ratings). These density ratings are stored in a database file  218  in storage  106 . 
     Subsequently, these statistics  214   a - f  can be transferred to another user  112   c  (or the same user  112   a ) in the form of time-elapsed graphs  218 . 
     In some embodiments of the present invention, users  112   a - c  are required to register with the server  102  before they can perform semantic word searches by uploading personal information to the server  102 , such as name, email address, age, and the like. This personal information is used to create a member structure  216 , which is stored in the database in storage  106 . 
       FIG. 3  is a flowchart illustrating method  300  steps of the present invention, including steps which may be performed determining a semantic density of textualized digital web media in accordance with the present invention. 
     The method  300  begins  302  with a list of domain names and/or ULRs from a user  112  being compiled  302  into a list of web media sources. These web media sources are queried  304  for textualized web media content. This textualized web media content is stored  306  in computer readable memory  106 . 
     Sentences  210  within the web media content  118  are identified and parsed  308  from the rest of the web media content  118 . The sentences  210  are stored  310  in an array datastructure. 
     Clauses  212  are identified  312  and parsed from the sentences  210 , then stored  314  in an array. 
     Dependencies between the clauses are determined using methodology further described below. The types of clauses and dependencies identified are used to increase the values of variables associated with those dependencies, a process through which semantic density is quantified  316 . A semantic density score for the each sentence, web media source, and the topic as a whole are calculated  318 . 
     In some embodiments, the sentences  210  are then queried  322  for instances of the topic, or keyword(s)  108 , input by a user  112 . The sentences  210  may subsequently be queried for instances of synonyms of the keyword(s)  108  ascertained by cross-referencing a digital thesaurus. Alternatively, instances of the keyword(s)  108  in the sentences  210  may be replaced with the synonyms, and the sentences  210  re-quantified  316  for semantic word density. 
     Finally, a graph of semantic word density for the topic or keyword(s)  108  is displayed  324  for the user  112 . 
       FIG. 4  is a block diagram illustrating one embodiment of a system which determines a semantic density of textualized digital web media in accordance with the present invention. The system  400  comprises modules largely configured to perform functions corresponding to the steps of the method  300  and computer program product embodiments. These correlations are further described in the claims. 
     Textualized web media content  118   a - c  is gathered by the block storer module  430  from web content sources at URLs identified by users  112 . A timeline  220 , with start and end points, is uploaded by a user  112  to the acceptor module  420 . Keywords  108  and URLs  204   a - c  are also uploaded to the system  400  by the user  112 . The user  112  may comprise a human being, group of human beings, or another DPD configured to use the system  400  for information retrieval purposes. 
     Density ratings  218  are stored in computer readable memory  106  by the indexing server  120  and output in graphical form to the DPD of a user  112 . 
     The system  400  may be said to form a search engine for information analysis and summary. 
       FIG. 5  is sentence content unit  210  subdivided into clauses in accordance with the present invention. Blocks of text in the present invention are parsed into sentences  210 . Sentences  210 , in some embodiments, are identified as strings of text separated by a period (.) following immediately by a space ( ). In other embodiments, the sentences  210  comprising a string of text not preceded and/or not followed by any text. 
     Certain unique identifiers are associated with each characteristic of each sentence  210 . For each sentence  210  a unique identifier is exclusively associated with a number of words in the sentence; a number of prepositions, postpositions, adjectives, adverbs, verbs, nouns, and grammatical conjunctions, by referencing words within the sentence with a dictionary in computer readable memory; a number of dependent clauses in the sentence; a number of independent clauses in the sentence; a number of ellipsis, a number of dashes (both en dashes and em dashes), and a number of commas, semicolons, and colons; a number of subjects and predicates in the sentence; a number of appositions in the sentence; a number of syllables in each word of the sentence by cross-referencing each word with the dictionary in persistent storage; and a number of alphanumeric characters in the sentence. 
     The magnitude, or value assigned to these unique idenifiers is representative of the number of times that each characteristic in the sentence in identified. These unique identifiers, along with unique identifiers representing the dependencies between sentence  210  clauses  504 - 510 , are used in determining the informative density, or semantic density of sentences  210 ; and; consequently, entire blocks of text and web source documents. 
     The sentence  210  comprises clauses identified by the system  400 . The sentences  210  are stored in computer readable memory as encoded content units  210 , which units are subjected to analysis by the indexing server  120 . 
     The keywords  108 , in the shown embodiment, comprise digitized search terms entered by a user of the search engine. Typically, the keywords  108  are received by the server  102  and relayed to the indexing server  120 . The keywords  108  may comprise phrases (one or more words) that relate to content which the user wishes to search, which content typically will relate to the subject matter of interest of the user. In response to reception of the keywords 1-8, the sever  120  may locate information archived in a content library under the control of the sever  120 . 
     The content library may be internal or external to the indexing server  120 . In some embodiments, the content library is in logical communication with the indexing server  120  via a distributed network, or through other means well-known to those of skill in the art. In the shown embodiment, the content units  210  are stored in a computer readable database controlled by the database server  101 , such as IBM DB2, Oracle, or SQL. 
     The keywords  108  are formulated into a database query and then associated with a query execution plan which identifies content units within the content library that satisfy the database query after searching textualized digital transcripts of the audio portions of the content units  108  stored in association with the content units  108 . These database queries may be generated using various languages including SQL, XPATH, and the like. The keywords  106   a - c  may also comprise other identifiers relevant to creating, or identifying, the proper query execution plan such as the title and the names of speakers featured in the content units  108  or other forms of additional information described above. 
     The keywords  106   a - c  may be formulated into database queries which are dynamic (meaning the query is generated as needed by a user with a form that is unknown until the query is received by the database server  101  and which form is likely to change between requests) or static (meaning the database query is predefined does not change form between requests, although the parametric data values of the query may change). 
     The dynamic database queries comprising the keywords  108 - c  may comprise more than just textualized words or phrases, but also logical bitwise operations that the indexing server  120  infers were intended by the user  112  such as NOR, OR, XOR, AND, NAND, and the like, as well as other information. The users  112  may be afforded a great deal of flexibility in searching for not just words and phrases, but combinations of words, phrases, and the like within the web media content. 
     Web media which satisfies a database query comprising the keywords  108  may be analyzed for informative or semantic density only after identifying instances of the keyword(s)  108  in the text. The web media content may be cached in computer readable memory and only those blocks of text satisfying the query execution plan relayed to the users  112  or modules configured to parse and quantify identifiers in the sentences  210  and clauses. 
       FIG. 6  is a Booch-relationship diagram illustrating the interrelationships  600  between the search engine, users, and web media pages in accordance with the present invention. In the shown embodiment, a search engine  602 , comprising the system  400 , is given information by users  112   a - d  who have been made aware of specific webpages  118   a - h  which they want processed by the search engine  602 . 
       FIG. 7  is a flowchart illustrating method steps of the present invention, including steps which may be performed determining a semantic density of textualized digital web media in accordance with the present invention. 
     The method  700  begins  702  and progresses as shown. A user  112  is prompted  704  to input a timeline with start and end points (i.e. start and end dates). The users  112  is also prompted  706  for keyword  108  (e.g. a topic). Web media content identified by the user  112  is scanned  708  by the indexing server  120 . 
     In the shown embodiment, if content has changed  710  from an earlier scan, information on web media page is scanned again and sentences  210  are parsed  712 . Clauses are identified  714 , and sentences are stored in arrays  716 . 
     The dependencies between the clauses are identified and used in calculating the informative density, or semantic density of the sentence  210  containing the clause(s). In some embodiments, b-tree and/or ASG graphs are used to determine these dependencies. In other embodiments, dictionaries and thesauri are referenced to determine relations and correlations between words and/or clause(s). 
     In some embodiments of the present invention, blocks of text identified by a user  112  are searched for instances of the keyword(s)  108  after the semantic word density fo the blocks of text is determined. In other embodiments, large collections of web media pages are searched for instances of the keyword(s)  108  before semantic word density is calculated. In still further embodiments, the blocks of text may be searched for instances of synonyms of the keyword(s)  108 , while in still further embodiments, instances of the keyword(s)  108  appearing the text may be substituted out and replaced with synonyms of the keyword(s) before performing further semantic word density analysis and calculation. 
       FIG. 8A  is a line graph  800  illustrating a time elapsed display of semantic density ratings for a general topic in accordance with the present invention. The general topic for which time elapsed semantic word density is shown is politics. The graph  800  shows word density, or the informative value of the discussion of politics, rising in 2009, slumping in 2010, and rising again in 2011. The sentences discussing politics average 41.1% of the maximum achievable density over the given 2009-2011 timeline  220 . 
       FIG. 8B  is a line graph  850  illustrating a time elapsed display of semantic density ratings for a specific topic in accordance with the present invention. 
     The specific topic for which time elapsed semantic word density is shown is the name of political candidate. Examples might include Barack Obama, Mitt Romney, and the like. The graph  850  shows word density, or the informative value of the discussion of this candidate, falling in 2009, rising in 2010, and falling again in 2011. The sentences discussing politics average 32.4% of the maximum achievable density over the given 2009-2011 timeline  220 . 
     The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.