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`determined rules. The opinion rating subsystem further analyzes the message
`information and assesses an opinion rating according to a plurality of pre-
`determined linguistic and associative rules. The central data store of the present
`invention comprises one or more non—volatile memory devices for storing electronic
`data including, for example, message information, results of analyses performed by
`the system and a plurality of other information used in the present invention.
`In a
`preferred embodiment,
`the central data store further comprises a relational
`database system for storing the information in the non—volatile memory devices. The
`data analysis system of the present invention may comprise an objective data
`collection subsystem, an analysis subsystem, and a report generation subsystem. The
`objective data collection subsystem interfaces with a plurality of pre-determined
`objective data sources to collect data which may be used to establish trends and
`correlation between real-world events and the communication expressed in the
`various electronic discussion forums. The analysis subsystem performs the analysis
`of the objective data and message information described above. The report
`generation subsystem generates reports of the analysis to end—users. The reports
`may comprise pre-determined query results presented in pre-defined report formats
`or, alternatively may comprise ad hoc reports based on queries input by an end-
`user.
`
`'
`
`(12):
`Brief Summary Text
`The method of the present invention comprises one or more of the steps of
`collecting a plurality of message information from a plurality of pre-determined
`electronic discussion forums; storing the plurality of message information in a
`central data store; categorizing the message information according to a plurality
`of pre-determined rules; assigning an opinion rating to the plurality of message
`information based on a plurality of pre-determined linguistic patterns and
`associative rules; collecting a plurality of objective data from a plurality of
`objective data sources; analyzing the message information and the objective data to
`identify trends in the pattern of behavior in pre-determined markets and the roles
`of participants in electronic discussion forums; and generating reports for end-
`users based on the results of the analyses performed by the present invention.
`
`Description Paragraph (15):
`Message Body—-the portion of an electronic message comprising the pseudonym's
`contribution to the electronic discussion. The Message Body generally comprises the
`data, opinions or other information conveyed in the electronic message,
`including
`attached documents or files. Header Information—-the portion of an electronic
`message not including the message body. Header Information generally comprises the
`transmission path and time/date stamp information,
`the message sender's
`information,
`the message identification number
`("message ID"),
`the subject. Buzz
`Level--for a community, a measure of activity within the community, as determined
`by the number of distinct pseudonyms.posting one or more messages over a given time
`frame. Connectivity-—for a community, a measure of its relatedness with other
`communities, as determined by the number of other communities in which a
`community's participants concurrently participate. Actor——descriptive name of the
`role that a pseudonym plays in the social networks of communities. Actors can be
`further classified according to the following definitions:
`Initiator——a pseudonym
`that commences a discussion, i.e., one that posts the first message leading to
`subsequent responses forming a dialog on a particular subject. Moderator——a
`pseudonym that ends a discussion, i.e., one that posts the final message closing
`the dialog on a particular subject. Buzz Accelerator-—a pseudonym whose postings
`tend to precede a rising buzz level in a community. Buzz Decelerator—-a pseudonym
`whose postings tend to precede a falling buzz level in a community. Provoker——a
`pseudonym that tends to start longer discussion threads; different from buzz
`accelerators in that the metric is one discussion thread, not
`the community's
`overall discussion level. ggy Signaler-—a pseudonym whose postings on a topic tend
`to precede a rising market for that topic. Sell Signaler——a pseudonym whose
`postings on a topic tend to precede a falling market for that topic. Manipulator--a
`pseudonym with little posting history except as Manipulators, whose combined
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`postings on one topic elevate the buzz level in the absence of external confirming
`events. Connector—-a pseudonym who posts on a high number of topics or a high
`number of communities. Market Mood—-a positive/negative market forecast derived
`from analysis of the patterns of actors‘ behavior.
`'
`V
`
`.Description Paragraph (17):
`the present invention is implemented using a system
`In a preferred embodiment,
`architecture as shown in FIG. 1. The system architecture comprises electronic
`discussion data system 10, central data store 20, and analysis system 30.
`Electronic discussion data system 10 interfaces via network 4 with selected
`electronic discussion forums 6 to collect electronic messages and analyze intrinsic
`data comprising the messages according to one aspect of the present invention.
`'
`Network 4 may be any communications network, e.g.,
`the Internet or a private
`intranet, and may use any suitable protocol for the exchange of electronic data,
`e.g , TCP/IP, NNTP, HTTP, etc. Central data store 20 is a repository for-electronic
`messages collected, objective data gathered from external sources and the results
`of the various analyses or reports produced by the system and method of the present
`invention. Central data store 20 may be implemented using any suitable relational
`database application program, such as, e.g., Oracle, Sybase and the like. Data
`analysis system 30 receives input from selected objective data sources for use in
`analyzing and quantifying the importance of the electronic discussion messages
`collected, and provides computer programming routines allowing end—users 9 to
`generate a variety of predefined and ad hoc reports and graphical analyses related
`to the electronic discussion messages. Each of the main systems comprising the
`system architecture of the present invention is described in more detail below.
`
`Description Paragraph (18):
`Central Data Store
`
`Description Paragraph (19):
`Central data store 20 comprises one or more database files stored on one or more
`computer systems.
`In a preferred embodiment, central data store 20 comprises
`message information database 22,
`topics database 23, objective data database 24,
`forum configuration_database 25, analysis database 26 and reports database 27, as
`shown in FIG. 1. Message information database 22 comprises the message information
`In a preferred embodiment, message
`‘collected by message collection subsystem 12.
`information database 22 comprises: a message ID, i.e., a number or other string
`that uniquely identifies each message; sender information, i.e., the pseudonym, e-
`_mail address or name of each message's author;-a posting time and date for each
`message (localized to a common time zone); a collection time and date for each
`message; a subject field, i.e.,
`the name of the thread or subject of each message;
`the message body for each message; an in—reply-to field, i.e.,
`the message ID of
`the message to which each message was a reply; and the source of the message.
`
`_Description Paragraph (20):
`The function and content of central data store 20's database files 23 27 are
`
`described in subsequent sections below.
`
`Description Paragraph (21):
`Electronic Discussion Data System
`
`Description Paragraph (22):
`As discussed above, electronic discussion data system 10 gathers certain messages
`and analyzes them according to the intrinsic information comprising the messages.
`Electronic discussion data system 10 comprises three subsystems: message collection
`subsystem 12, message categorization subsystem 14 and opinion rating subsystem 16.
`Message collection subsystem 12 collects message information from data sources and
`stores the information in central data store 20 for later analysis. Message
`categorization subsystem 14 extracts information about each message in central data
`store 20 and categorizes the messages according to a plurality of pre—defined
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`topics. The subsystem analyzes all aspects of each message and determines if the
`message is relevant to one or more of the topics that the system is currently
`tracking. A relevancy ranking for each message is stored in central data store 20
`for each topic indicating the strength of the message's relation to each topic.
`Further analysis of the collected message information is carried out by opinion
`rating subsystem 16 to determine whether the message conveys a positive, neutral or
`negative opinion regarding the related topic. Each of the subsystems of electronic
`discussion data system 10 are described in more detail below.
`
`Descrippion Paragraph (24):
`Message collection subsystem 12 collects electronic message information from the
`designated electronic discussion forums and passes the collected messages to
`central data store 20 and to message categorization subsystem 14, as shown in FIG.
`’1. The collected messages comprise records stored in message information database
`22 in central data store 20. Database 22 comprises records including message header
`information and the message body.
`In a preferred embodiment, each field comprising
`message header information comprises a separate field of a record in database 22.
`The architecture used in a preferred embodiment of the present invention for
`implementing message collection subsystem 12 is shown in the schematic diagram in
`FIG. 2. This architecture supports multiple configurations for data collection and
`is highly scalable for gathering large or small amounts of message information.
`FIG.
`2 illustrates some of the configurations that may be used in a preferred
`embodiment of message collection subsystem 12.
`
`Description Paragraph (25):
`As shown in FIG. 2,
`the message collection subsystem consists of several components
`that function together to collect information from electronic discussion forums 61
`and 62 or discussion data files 63 and 64 on distributed networks 41 44. Although
`shown as separate discussion forums, data files and networks, it would be apparent
`to one skilled in the art that discussion forums 61 and 63 and data files 63 and 64
`
`could be the same discussion forum or data file, and networks 41 44 could comprise
`a single distributed network, such as the Internet. Components of message
`collection subsystem 12 include message collector programs and message processor
`programs running on one or more computer systems. The computer systems used by
`message collection subsystem 12 comprise any suitable computers having sufficient
`processing capabilities, volatile and non—volatile memory, and support for multiple
`communications protocols.
`In a preferred embodiment,
`the computer systems used by
`.message_collection subsystem 12 comprise UNIX—based servers such as available from
`Sun Microsystems, or Hewlett-Packard and the like. All of the subsystem components
`can be replicated within a single computer system or across multiple computer
`systems for overall system scalability.
`
`g
`Description Paragraph (26):
`In a preferred embodiment, message processor programs, e.g., message processor 121a
`and 121b, are in communication with database 22, which is part of central data
`store 20 (not shown in FIG. 2).
`In FIG. 2,
`the message processors and central data
`store are protected from unauthorized access by firewall security system 122. Other
`components of message collection subsystem 10 are located at various points in the,
`architecture, as described below. As would be apparent
`to one of ordinary skill in
`the art, firewall 122_is provided for security and is not technologically required
`for operation of the present invention. Message processors 121a and 121b receive
`information from the message collectors and store the information in the database
`22 for later processing. As shown in FIG. 2, message processors 121a and 121b may
`service more than one message collector program to facilitate processing of a large
`volume of incoming messages.
`Inbound messages are held in a queue on the message
`processors, allowing message processors 121a and 121b to receive many more messages
`from the message collectors than they can actually process for storing in database
`22. This architecture allows the rapid collection of millions of messages from tens
`of thousands of discussion forums without excessive overloading of the computer
`systems.
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`Description Paragraph (27):
`As is known in the art, each discussion forum or data file may have a unique
`message format. For example, an electronic message from one discussion forum may
`place the date field first,
`the message ID second, and the other header and body
`data last. A different discussion forum may choose to display the message ID first,
`followed by the pseudonym of the participant, and the message body. Moreover, each
`type of discussion forum has its own communications protocol. For example,
`the
`communications protocol for an interactive discussion forum (e.g., a chat session)
`is not the same as the communications protocol for USENET news groups. The message
`format and protocols need not be static, i.e., as discussion forums evolve,
`different data structures and protocols may be implemented. To accommodate such
`changes, each message collector receives configuration information from forum
`configuration database 25 in central data store 20, either directly or via the
`message processor systems. The configuration information indicates the data source,
`i.e., the discussion forum or discussion file,
`from which messages will be
`collected. The configuration information further comprises programming instructions
`tailored for each individual data source to allow the message collector program to
`communicate with the data source and extract and parse the message information.
`Accordingly, message collectors can support a wide variety of protocols utilized by
`discussion forums including, e.g., HTTP, NNTP,
`IRC, SMTP and direct file access.
`In
`a preferred embodiment,
`the general programming instructions are written the Java
`programming language with parsing instructions written in Jpython scripting
`language. By storing the configuration information in a centralized location, i.e.,
`central data store 20, management of the message collectors is simplified.
`Accordingly, when the data structure for a particular discussion forum changes,
`configuration information needs to be modified only once.
`
`the
`
`Description Paragraph (29):
`As noted above,
`there are several ways to implement the architecture supporting
`message collection subsystem 12.
`In one implementation, message collector programs,
`shown in FIG.
`2 as local message collectors 123a and 123b, are part of local area
`network ("LAN") 124 and are authorized access through firewall 122. Local message
`collector 123a interfaces through network 41 to collect messages from discussion
`forum 61 and local message collector 123b has direct access to discussion data file
`63. The latter configuration may be implemented, e.g.,
`if the operator of message
`collection subsystem 12 also hosts a community for-message discussion forums. As
`shown in FIG. 2, a message collector may collect messages from multiple discussion
`forums. For example, as shown in FIG. 2,
`local message collector 123b also
`interfaces through network 41 to collect messages from discussion forum 61.
`
`Description Paragraph (30):
`In an alternative implementation,
`such as remote
`message collector programs,
`are run on external networks. As shown in FIG. 2,
`message collectors 125a and 125b,
`the remote message collectors are not part of LAN 124 and do not have direct access
`to the message processor programs running behind firewall 122. For security
`reasons, proxy servers 126a and 126b are used to interface with message processor
`121b through firewall 122. Functionally,
`remote message collectors operate in the
`same manner as the local message collectors. That is,
`remote message collectors
`125a and 12Sb receive configuration information from central data store 20 (via
`proxy servers 126a and 126b, respectively). Moreover,
`remote message collectors may
`collect messages from discussion forums over a network or directly from discussion
`data files, as shown in FIG. 2. Use of remote message collectors allows for
`geographic distribution and redundancy in the overall message collection subsystem
`architecture.
`
`Description Paragraph (32):
`Message categorization subsystem 14 analyzes the data collected from discussion
`forums and categorizes the messages into meaningful groupings, i.e., parent topics
`and topics, according to predefined rules as described below.
`In a preferred
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`embodiment, message categorization subsystem 14 retrieves message information from
`database 22 and topic information from central data store 20 and stores results o
`the categorization process in database 22. Alternatively, message categorization
`subsystem 14 may receive input directly from message collection subsystem 12 for
`immediate processing into categories.
`
`_
`Description Paragraph (33):
`Topics database 23 comprises representations of real world topics that are being
`tracked and analyzed by the system and method of the present invention. FIG.
`3
`shows the hierarchical data structure used in a preferred embodiment of database
`23.
`In a preferred embodiment, abstract root 231,
`shown in FIG.
`3 as the top—level
`of the hierarchy,
`is not an actual topic stored in database 23_and is shown only to
`illustrate the hierarchy. Similarly, branches 232 234 are shown in FIG.
`3
`to
`conceptually show the relationship between topics stored in database 23.
`Accordingly, branch 232 indicates that some topics stored in database 23 may relate
`consumer entertainment, branch 233 indicates other topics relate to stock markets,
`and branch 234 may include other topics, such as, e.g.,
`food, sports,
`technology
`adoption, and the like. As shown in FIG. 3,
`the hierarchy comprises one or more
`parent topics, such as parent topics 235 (related to books), parent topic 236
`(related to movies), parent topic 237 (related to market
`indexes) and parent topic
`238 (related to companies). Topics in the hierarchy are the last level, such as,
`topic 235a (Tears of the Moon),
`topic 235b (The Indwelling),
`topic 235c (Hot Six)
`and topic 235d (The Empty Chair). As shown in FIG. 2,
`topics 235a 235d are related
`to each other by parent topic 235 (books).
`
`Description Paragraph (34):
`In a preferred embodiment of the present invention, message categorization
`subsystem 14 assigns a relevance ranking for each topic to each message collected
`by message_collection subsystem 12. The relevance ranking is determined based on a
`set of predefined rules stored in database 23 for each topic; The rules comprise a
`series of conditions defining information relevant to the topic, having an
`associated weighting to indicate the strength a particular condition should have in’
`determining the overall relevance rank of the message with respect to the topic.
`Messages that need categorization are processed by message categorization subsystem
`14 synchronously, i.e.,
`the rules for each topic are applied to each message
`regardless of the relevance ranking for prior topics. The elements of each message,
`including subject, source, and content are processed against the conditions of each
`topic in the database. Based on the conditions that are satisfied and the weights
`of those conditions, a relevance rank for each topic is assigned to each message.
`As messages are processed,
`their relevance ranking for each topic is updated in
`message information database 22 in central data store 20.
`
`Description Paragraph (39):
`Opinion rating subsystem 16 extracts message information from database 22 in
`central data store 20 and assigns an opinion rating for each message by analyzing
`textual patterns in the message that may express an opinion. The textual patterns
`are based on linguistic analysis of the message information. For example, if the
`message body includes words such as "movie" and "awful" in the same sentence or
`phrase and the message had a high relevancy ranking for the topic “The Perfect
`Storm" the message may be expressing a negative opinion about
`the movie. Textual
`pattern analysis software, such as available from Verity Inc, of Mountain View,
`Calif., may be used to assign the opinion rating for each message. Such passive
`opinion polling is useful for market analysis without the need for individually
`interviewing active participants in a survey. Once the rating process is complete,
`the rating for each opinion processed is stored in database 22 in central data
`store 20.
`
`Description Paragraph (40):
`Data Analysis System
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`Description Paragraph (41):
`Data analysis system 30 comprises objective data collection subsystem 32, analysis
`subsystem 34 and report generation subsystem 36, as shown in FIG. 1. The overall
`goal of data analysis system 30 is to identify and predict trends in actual markets
`based on the electronic discussion data being posted to various electronic
`discussion forums and to provide reports for end—users 9 of the system and method
`of the present invention.
`‘
`
`Description Paragraph (42):
`1. Objective Data Collection Subsystem
`
`Description Paragraph (43):
`Objective data collection subsystem 32 collects objective data from both
`traditional and electronic sources and stores the information in database 24 on
`central data store 20 for later analysis. Objective data sources 8,
`shown in FIG.
`1, may include for example, market data such box office sales for recently released
`movies, stock market activity for a given period,
`television viewer market share
`(such as Nielson ratings), and other such objective data. The specific data
`collected from each objective data source depends on the nature of the market being
`analyzed. For example, objective data on the stock market may include: a company's
`name; its web home page address, i.e., universal resource locator;
`ticker symbol;
`trading date; opening price; high price;
`low price; closing price and volume.
`In
`other markets,
`the objective data may include: sales, measured in units sold and/or
`revenue generated; attendance at events; downloads of related software and media
`files; press release date,
`time and key words; news event date; and the like. The
`objective data is used by analysis subsystem 34 to identify and predict trends and
`correlation between real world events and electronic discussion data, as described
`below.
`
`Description Paragraph (45):
`Analysis subsystem 34 performs analysis of the information collected by the message
`collection subsystem 12 and objective data collection subsystem 32, and the
`categorization and opinion information determined by message categorization
`subsystem 14 and opinion rating subsystem 16, respectively. Analysis subsystem 34
`determines the existence of any correlation between discussion forum postings and
`market activity for each topic that the system is currently tracking. The results
`of the analysis are stored in the analysis database 26 in central data store 20 for
`eventual presentation to end—users 9. Analysis subsystem 34 examines the internal
`behavior of communities and correlates individual and group behavior to the world
`external to the communities using a variety of analysis techniques with a variety
`of goals. Analysis subsystem 34 identifies and categorizes actors by measuring the
`community's response to their postings; measures and categorizes the community's
`mood; correlates actors’ behavior and the communities’ moods with objective data
`sources; and forecasts the markets‘ behavior, with confidence estimates in various
`timeframes. Identifying and tracking both the actors and the community mood is
`important, because the effect of an actor's message depends in part on the mood of
`the community. For example, an already-nervous community may turn very negative if
`a bpy signaler or other negative actor posts a message, while the same message from
`the same person may have little effect on a community in a positive mood. The
`following sections describe the patterns sought in the analysis and describes how
`the community behaves after postings by each local pseudonym associated with the
`patterns.
`
`Description Paragraph (47):
`Actors are classified by correlating their postings with objective data, which is
`external to the electronic forum. Changes in the objective data (e.g., stock price
`changes,
`increased book sales, etc.) are tracked during several discrete short time
`periods throughout a longer time period, such as day. A score is assigned to each
`pseudonym posting messages related to a given topic based on the change observed in
`the objective data from the preceding discrete time period. A pseudonym's score may
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`in a
`be high, medium or low, depending on the magnitude of the change. For example,
`preferred embodiment, pseudonyms who tended to post messages just prior to major
`increases in stock price, receive a high positive scores; while those whose
`postings tended to precede major drops have the lowest negative scores. The scores
`assigned to a pseudonym during the longer time period are aggregated into a-
`composite score for the pseudonym.
`
`Description Paragraph (49):
`Two of the more interesting classifications made by analysis subsystem 34 identify
`buzz accelerators and buzz decelerators. Because of the correlation identified in
`
`some markets between the level of discussion in a community and the objective,
`real—world events,
`identification of buzz accelerators and decelerators can be used
`to predict the probable outcome of real—world events. For example,
`if a local
`pseudonym were identified as a buzz accelerator for electronic discussion forums
`related to the stock market, whenever that local pseudonym posts a message to such
`a forum, one would expect a rise in the discussion level, and the correlating drop
`in stock prices. Related, but not synonymous, classes of actors are ppy signalers
`and sell signalers. Such actors tend to post messages at,a time preceding a rising
`or falling market for that topic.
`In contrast to buzz accelerators or decelerators,
`ppy and sell signalers do not necessarily tend to reflect or precede rising levels
`of electronic discussion on the forums.
`
`Description Paragraph (51):
`Analysis subsystem 34 tracks and observes the behavior characteristic of the
`pseudonyms posting messages to electronic discussion forums and assigns a
`reputation score indicating their categorization.
`In a preferred embodiment,
`‘reputation score comprises an array of ratings for each of the possible
`categorizations. From the reputation score, composite views of the tendencies-of
`the pseudonyms can be formed to graphically illustrate the pseudonym's reputation
`in a given community. An example of one such composite view is shown in FIG. 4,
`wherein a pseudonym's reputation as a buzz accelerator/decelerator is plotted
`against its reputation as a buy/seller signaler. As shown in FIG. 4, pseudonym A
`has a strong tendency as a ppy signaler and is a buzz accelerator, but not a strongv
`buzz accelerator.
`In contrast, pseudonym B has strong tendencies as both a sell
`signaler and a buzz decelerator in the market. The impact of the classifications
`depends; of course on the market
`involved, as discussed previously.
`I
`
`the
`
`Description Paragraph (53):
`As discussed above, pseudonym's classifications are useful to the extent they can
`quantify the tendencies of the various actors in a community. However,
`the impact
`of such actors on the community depends not only on the tendencies of the actors,
`but on the overall mood of the community. The measure of a community's mood is
`determined from the change in discussion levels in the community. The mood assigned
`is based on observed trends for the associated topic. For example, when discussion
`levels rise in stock market forums,
`the rise is usually accompanied by a drop in
`indicating a negative mood
`_ stock market prices due to increased selling activity,
`in the community. Similarly, an increase in discussion levels for a movie topic may
`indicate a generally positive mood for the community. Other indicators of community
`mood include the number of new participants in a community, which correlates to an
`increased interest in the community's topic. Moreover,
`the combined positive and
`negative influence scores of actors in a community is an indicator of the its
`overall sentiment. Another factor indicating a community's mood is its turnover
`rate,
`i e.,
`the number of new participants versus the number of old participants,
`indicates the depth of interest in the community's topic.
`
`.
`Description Paragraph (62):
`the number of
`As with any high-frequency, high—volume data mining challenge,
`potential variables is enormous and the applicable techniques are many. To simplify
`this problem,
`the system and method of the present invention reduces the data sets
`as much as possible before analysis. Accordingly, on the assumption that there are
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`the vast majority
`a very small number of opinion leaders relative to participants,
`of participants whose postings did not occur near objective data inflection points,
`i.e., sharp changes in the objective data, are eliminated. This greatly reduces the
`amount of data that is further analyzed by the system and method of the present
`invention. The period of time over which inflection points are identified has a
`great impact on which patterns can be identified and usefulness of the resulting
`data. For example, stock price movement and other markets are known to have fractal
`patterns, so they have different inflection points depending on the time frame
`chosen. Accordingly, different inflection points will be identified if the period
`is weekly, monthly, or yearly. The more volatile a market is,
`the more inflection
`points can be found.
`
`’
`
`Description Paragraph (69):
`On Balance Volume (OBV) uses stock trading volume and price to quantify the level
`of buying and selling in a security.
`In a preferred embodiment of the present
`invention, OBV is used, e.g., by substituting the number of discussion participants
`for the stock volumeu In this context, QBV is a negative indicator, i.e., when it
`‘is rising, price tends to fall; when it falls, price tends to rise.
`
`Description Paragraph (70):
`Moving Averagg Convergence—Divergence
`
`-
`.
`Description Paragraph (7l):
`is a technical analysis that may be
`Moving Average Convergence-Divergence (MACD)
`applied to the discussion levels in the communities. MACD generates signals by
`comparing short-term and long—term moving averages;
`the points at which they cross
`one another can be buy or sell signals, depending on their directions. MACD can
`signal when a community's discussion level rises above the recent averages, which
`is often an indicator of rising nervousness.
`
`.
`
`Description Paragraph (73):
`In one embodiment of the present invention an "80/20 rule," supported by social
`network research,
`is used wherein only the 20 percent of participants whose posts
`are "closest" (in time)
`to significant objective data inflection points are
`analyzed. While this method simplifies the task of analyzing the data,
`there is
`some risk that opinion—leading groups may be overlooked. Such groups comprise
`individuals that do not consistently post at the same time, but as a group exhibit
`the characteristics of individual opinion leaders. For example, it is possible Bob,
`Sam and George form a positive opinion leader group,