JP6101985B2 - Program, terminal device and data processing method - Google Patents

Description

  The present invention relates to a technique for visualizing data characteristics, and more particularly to a technique for visualizing data characteristics transmitted or received in communication on a network.

  Conventionally, techniques for visualizing communication have been proposed. For example, in the technique described in Patent Document 1, in order to improve productivity within the organization, the face-to-face communication is actually observed, and the communication style of the personal organization belonging to the organization, the communication style of the association organization included in the organization's communication style organization is determined. Visualized. Specifically, based on interaction data collected from sensors, visualization of communication is realized by plotting individual communication styles on a two-dimensional map.

  Moreover, in patent document 2, the difference of values with a communication partner and the affirmation degree for the conversation topic are estimated by using the strength of agreement or disagreement of a phrase used for replying a conversation, and the affirmation degree is indicated by an icon. Visualization is realized by converting to display attributes.

  Also, in Patent Document 3, communication performed by a plurality of means such as e-mail logs and conference recordings within or between organizations is recorded, and integrated into a common index called information capture time to realize communication visualization Techniques to do this are disclosed.

  Non-Patent Document 1 discloses a technique for analyzing communication by a mobile phone from a transmission / reception history and visualizing human relationships as a network.

JP 2012-128882 A JP 2006-209332 A JP 2006-127142 A

Eagle N, Pentland A, “Reality Mining: Sensing Complex Social Systems”, J of Personal and Ubiquitous Computing, July 2005 (Eagle, N., and Pentland, A ., "Reality Mining: Sensing Complex Social Systems", J. of Personal and Ubiquitous Computing, July 2005)

  However, since the technology described in Patent Document 1 is based on the premise that communication is actually observable, it is not easy to adapt it to the purpose of visualizing communication features generated on SNS and communication tools. .

  Moreover, since the technique described in Patent Document 2 specializes in evaluating differences in values in communication interaction between users, it is not suitable for visualizing communication characteristics such as SNS.

  Moreover, in the technique described in Patent Document 3, the transition of organizational communication is displayed and diagnosed based on the temporal transition of communication, but it is applicable to analysis including detailed parameters such as communication quality and status. I can't. Furthermore, features from multiple viewpoints cannot be handled simultaneously. This also applies to Non-Patent Document 1.

  Conventionally, it is not easy to detect what kind of communication an individual user is performing in SNS and measure the communication skill of the individual user based on the detected communication.

  The present invention has been made in view of such circumstances, a program, a terminal device, and data processing capable of visualizing communication skills of an individual user and presenting information indicating a service corresponding to the operation skill of the user It aims to provide a method.

  (1) In order to achieve the above object, the present invention takes the following measures. That is, the program of the present invention is a program for presenting information indicating a service corresponding to a user's operation skill, and is a process for obtaining user operation data that is operated by the user and is labeled based on at least one user variable. And a process for generating an operation skill feature vector indicating a user's operation skill using the user variable of the acquired user operation data as a parameter, and network data generated using a network variable of the network data operated on the network as a parameter Processing for acquiring a feature vector; processing for calculating a similarity between the operation skill feature vector and the network data feature vector; and at least one network data feature vector as a display candidate based on the calculated similarity. Elect A computer is caused to execute a series of processes including: a process; a process of mapping the selected network data feature vector into a multidimensional space; and a process of displaying the mapped network data feature vector on a screen. To do.

  As described above, the operation skill feature vector indicating the user's operation skill is obtained by acquiring user operation data that is operated by the user and is labeled based on at least one user variable, and using the user variable of the acquired user operation data as a parameter. To obtain the network data feature vector generated using the network variable of the network data operated on the network as a parameter, calculate the similarity between the operation skill feature vector and the network data feature vector, and calculate the similarity Based on the above, at least one network data feature vector as a display candidate is selected, the selected network data feature vector is mapped to a multidimensional space, and the mapped network data feature vector is displayed on the screen. The communication skills of over The becomes possible to visualize. Thereby, it is possible to present information indicating a service corresponding to the user's operation skill.

  (2) In the program of the present invention, the user variable indicates what function on the system the user operation data is handled on, and is based on a system function feature skill vector characterized by the function on the system. Then, the operation skill feature vector is generated.

  As described above, the user variable indicates what function on the system the user operation data is handled, and generates an operation skill feature vector based on the system function feature skill vector characterized by the function on the system. Therefore, based on the label data given to the text obtained from each category, it becomes possible to extract the degree of affinity of the text posted by a specific user with respect to the system feature as a skill. For example, if a user has posted many comments on a “SYNCHRONOUS” communication tool (such as chat), it can be understood that the user is familiar with the “SYNCHRONOUS” service. By generating the system function skill vector of the user based on this frequency information, the frequency of each system function feature variable can be expressed.

  (3) In the program of the present invention, the user variable indicates in what system situation the user operation data is handled, and based on a situation feature skill vector characterized by the situation on the system. The operation skill feature vector is generated.

  In this way, the user variable indicates in what system situation the user operation data is handled, and the operation skill feature vector is generated based on the situation feature skill vector characterized by the situation on the system. Based on the label data assigned to the text obtained from each category, it is possible to extract the degree of affinity for the situation feature of the text posted by a specific user as a skill. For example, if a user has posted many comments on a “PUBLIC” communication tool (such as chat), it can be understood that the user is familiar with the “PUBLIC” service. By generating the user's situation feature skill vector based on this frequency information, the frequency of each situation feature variable can be expressed.

  (4) Further, in the program of the present invention, the user variable indicates how the user operation data is handled, and based on a user attitude skill vector characterized by the user attitude, An operation skill feature vector is generated.

  Thus, the user variable indicates what kind of user's attitude the user operation data is handled, and generates the operation skill feature vector based on the user attitude skill vector characterized by the user's attitude. Based on the label data assigned to the text obtained from the category, it is possible to extract the degree of affinity of the text posted by a specific user with respect to the user attitude feature as a skill. For example, if the user has posted many comments on “THANK” or “GREET”, it can be understood that the user has many experiences and skills for social comment posting. Based on this frequency information, a user attitude skill vector can be generated, and the frequency of each user attitude skill vector variable can be expressed.

  (5) Further, the program of the present invention selects an important word included in user operation data, and generates the operation skill feature vector based on an important word vector having a word having a high importance as an element. And

  In this way, key words included in user operation data are selected, and operation skill feature vectors are generated based on key word vectors whose elements are words of high importance, so the communication skills of individual users are clearly expressed. It becomes possible to do. Thereby, it is possible to present information indicating a service corresponding to the user's operation skill.

  (6) The program of the present invention further includes a process of inputting user operation data, and a process of labeling the input user operation data based on at least one user variable. And

  As described above, the method further includes a process of inputting user operation data and a process of labeling the input user operation data based on at least one user variable. Can be performed.

  (7) Moreover, the terminal device of the present invention is a terminal device that presents information indicating a service corresponding to the user's operation skill, and is operated by the user and labeled based on at least one user variable. A data acquisition unit that acquires data, an operation skill feature vector generation unit that generates an operation skill feature vector indicating a user's operation skill using a user variable of the acquired user operation data as a parameter, and a network operated on the network A network data feature vector generated using a network variable of data as a parameter is acquired, a similarity calculation unit that calculates a similarity between the operation skill feature vector and the network data feature vector, and based on the calculated similarity , At least one network as a display candidate A mapping unit that selects a network data feature vector, maps the selected network data feature vector to a multidimensional space, and a display unit that displays the mapped network data feature vector on a screen. To do.

  As described above, the operation skill feature vector indicating the user's operation skill is obtained by acquiring user operation data that is operated by the user and is labeled based on at least one user variable, and using the user variable of the acquired user operation data as a parameter. To obtain the network data feature vector generated using the network variable of the network data operated on the network as a parameter, calculate the similarity between the operation skill feature vector and the network data feature vector, and calculate the similarity Based on the above, at least one network data feature vector as a display candidate is selected, the selected network data feature vector is mapped to a multidimensional space, and the mapped network data feature vector is displayed on the screen. The communication skills of over The becomes possible to visualize. Thereby, it is possible to present information indicating a service corresponding to the user's operation skill.

  (8) Moreover, the terminal device of this invention is further provided with the discriminator which labels the input user operation data based on at least one user variable.

  In this manner, since the discriminator that performs the labeling on the input user operation data based on at least one user variable is further provided, the input data can be automatically labeled.

  (9) The data processing method according to the present invention is a data processing method for presenting information indicating a service corresponding to a user's operation skill, which is operated by the user and is labeled based on at least one user variable. Obtaining the user operation data, generating an operation skill feature vector indicating the user's operation skill using the user variable of the acquired user operation data as a parameter, and a network variable of the network data operated on the network A network data feature vector generated using as a parameter, a step of calculating a similarity between the operation skill feature vector and the network data feature vector, and a display candidate based on the calculated similarity At least one network Selecting at least a data feature vector; mapping the selected network data feature vector to a multidimensional space; and displaying the mapped network data feature vector on a screen. .

  As described above, the operation skill feature vector indicating the user's operation skill is obtained by acquiring user operation data that is operated by the user and is labeled based on at least one user variable, and using the user variable of the acquired user operation data as a parameter. To obtain the network data feature vector generated using the network variable of the network data operated on the network as a parameter, calculate the similarity between the operation skill feature vector and the network data feature vector, and calculate the similarity Based on the above, at least one network data feature vector as a display candidate is selected, the selected network data feature vector is mapped to a multidimensional space, and the mapped network data feature vector is displayed on the screen. The communication skills of over The it is possible to visualize. Thereby, it is possible to present information indicating a service corresponding to the user's operation skill.

  According to the present invention, it becomes possible to visualize the communication skill of an individual user. Thereby, it is possible to present information indicating a service corresponding to the user's operation skill.

It is a figure which shows schematic structure of the data processing system which concerns on this embodiment. It is a figure which shows a GUI image. It is a flowchart which shows the operation | movement of coding (labeling). It is a flowchart which shows operation | movement of the feature vector generation module. It is a figure showing the frequency of each system function characteristic variable. It is a flowchart which shows a system function vector extraction process. It is a figure showing the frequency of the labeling result of each situation characteristic variable. It is a flowchart which shows a situation feature vector extraction process. It is a figure showing the frequency of each user attitude skill vector variable. It is a flowchart which shows a user attitude | position vector extraction process. It is a figure which shows a mode that a feature vector is grouped by clustering. It is a figure which shows a mode that the feature vector was visualized hierarchically. It is a flowchart which shows operation | movement of a service feature extraction and presentation module. It is a flowchart which shows a similarity calculation process. It is a figure expressing a system function skill vector. An example in which system features from M1 to M4 are collectively visualized is shown. It is a figure expressing a situation feature skill vector. An example in which system features from S1 to S2 are collectively visualized is shown. It is a figure expressing a user attitude skill vector. An example in which system features A1 to A16 are collectively visualized is shown.

  Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram showing a schematic configuration of a data processing system according to the present embodiment. This data processing system includes a communication data collection module 5, a database 7, a labeling module 9, a feature vector generation module 11, a service feature extraction / presentation module 21, a similarity calculation / presentation module 25, an individual skill extraction module 27, and a display module. 23.

  The communication data collection module 5 collects data from SNS (Social Networking Service) 1 and e-mail / call data 3. For example, data can be collected by crawling using an API (Application Programming Interface). In this case, an API of an Internet radio station or an API of Twitter (registered trademark) can be used.

  For the input, for example, post text information for a certain period of SNS, comment data of multimedia service, electronic mail interaction data, chat data, and the like can be used. Further, when a chat function, a blog function, or the like exists in the same SNS, it can be handled as the same data, or can be divided for each function. The data collected in this way is stored in the database 7.

  The labeling module 9 performs coding (labeling) on the data collected by the communication data collection module 5. This coding provides a GUI (Graphical User Interface) on the WEB and can be performed manually by an operator and stored in the DB. For example, the coding standard can use the communication classification scheme described in Related Document 1.

[Related Literature 1]
Susan C. Herring (2007), A Faceted Classification Scheme for Computer-Mediated Discourse.Language@Internet.http: //www.languageatinternet.org/articles/2007/761
In the present invention, coding is performed based on the following variables.

[System feature variables]
M1 (Synchronicity), M2 (Message transmission), M3 (Persistence of transcript), M4 (Size of message buffer), M5 (Channels of communication), M6 (Anonymous messaging), M7 (Private messaging), M8 (Filtering), M9 (Quoting), M10 (Message format)
In these system characteristic variables, a value can be set for each variable. For example, in the case of M1, a label such as “Synchronous” at the time of 1 and “Asynchronous” at the time of 2 can be given.

[Situation feature variable]
S1 (Participation Structure), S2 (Participant characteristics), S3 (Purpose), S4 (Topic or Theme), S5 (Tone), S6 (Activity), S7 (Norms), S8 (Code)
These situation feature variables can be free descriptions or can be given options in advance.

  FIG. 2 is a diagram illustrating a GUI image. For example, as shown in FIG. 2, communication data is displayed on the left side of the screen, and coding results can be input on the right side. The input method can be input with a check box or selected with a touch panel. The coding itself can be performed by multiple people. In that case, it is possible to compare a plurality of results, and to re-present to the coder the result coincidence rate and the different results.

  In addition to the above variables, scene variables, user attitude variables, and the like can be freely set as variables. For example, a season variable, a season, a time zone, a weather, etc. can be utilized. For example, as the user attitude variable, a speaker's attitude (presentation / approval, rejection) or the like can be used. For example, it can be set as follows using the technique of Related Document 2 below.

[Related Literature 2]
Herring, SC, Das, A., & Penumarthy, S. (2005). CMC act taxonomy. Http://www.slis.indiana.edu/faculty/herring/cmc.acts.html
A1 (Inquire), A2 (Request), A3 (Invite), A4 (Desire), A5 (React), A6 (Manage), A7 (Direct), A8 (Accept), A9 (Apologize), A10 (Repair), A11 (Reject), A12 (Elaborate), A13 (Thank), A14 (Inform), A15 (Claim), A16 (Greet)
The labeling module 9 can be provided with a function as a discriminator such as SVM (Support vector machine) so that coding can be performed automatically. When coding is automatically performed, prior information registered in the system in advance can be used, or learning data can be collected and coded in advance, and automatic labeling can be performed by a discriminator. For example, when the information obtained by SNS (A) is fixed from M1 to M10, similar information can be automatically given to other data obtained by SNS (A).

  In addition, labeling can be automatically applied to S1-S8 and the like. For example, when a coding result for a large amount of communication data can be accumulated as learning data, a label can be automatically given by a discriminator. For example, the communication data to which the coding result S1 is given is converted into a feature vector by TFIDF, and a label can be automatically given by determining whether S1 is correct or not by SVM.

  FIG. 3 is a flowchart showing an operation of coding (labeling). First, data is acquired via the communication data collection module 5 (step S1), and it is determined whether there is a discriminator (step S2). If there is a discriminator, the process proceeds to step S7. On the other hand, if there is no discriminator in step S2, it is determined whether there is label data (step S3). If there is label data, the process proceeds to step S6. If there is no label data, the label data is acquired (step S4), displayed on the GUI (step S5), and a discriminator is generated based on the data labeled by the operator (step S6).

  Here, for example, when discriminating whether or not the label A is using the “Support Vector Machine”, the discriminator extracts the important word from the text data group to which the label A is given, and determines the frequency of the important word. Based on the feature vector. (For example, Bag of Words based on TF / IDF) Learning data group with label A and its feature vector group as positive data, learning data without label data A and its feature vector group as negative data It is possible to generate a discriminator for determining whether or not the label A is used. Then, a label is given (step S7), and the process ends.

  In FIG. 1, the feature vector generation module 11 includes a situation feature skill extraction function 13, a system function skill extraction function 15, a user attitude skill extraction function 17, and an important word extraction function 19. Based on the data, the communication data is featured. For example, the input coding result can be expressed as a multidimensional vector using the input value of each variable as a parameter. For example, system function features, situation features, and user attitude features can be extracted as vectors. Also, these vectors can be combined and extracted as a feature vector. Further, communication data is selected by selecting an important word from the accumulated communication data based on the TFIDF method, and calculating the frequency of the word as each element in the data with a word having high importance as a vector element. Can be vectorized.

  FIG. 4 is a flowchart showing the operation of the feature vector generation module. First, a coding result and communication data are input (step T1). Next, a system function feature vector is extracted (step T2). Next, a situation feature vector is extracted (step T3). Next, a user attitude vector is extracted (step T4). Next, an important word vector is extracted (step T5). Then, the extracted vectors are integrated (step T6), and the process ends. Next, processing for extracting a vector as described above will be described.

[System function vector extraction processing]
Based on the label data assigned to the text obtained from each category, what kind of system function the communication data is performed on is extracted as a feature. For example, if a single communication service is used as the unit of analysis, if the user has posted many comments on a “SYNCHRONOUS” communication tool (such as chat), it will be “SYNCHRONOUS” on that service. It can be understood that the service is often used. Based on this information, a user system function vector is generated.

  FIG. 5 is a diagram showing the frequency of each system function characteristic variable. When visualizing in this way, in order to reduce the difference in the number of analysis data, normalization can be performed for all the number of analysis units. Further, it is not always necessary to execute processing in service units, and macro communication data generated in a certain service may be used as one analysis unit.

  FIG. 6 is a flowchart showing system function vector extraction processing. First, label data is acquired (step P1), and the number of elements is counted (step P2). Next, it is determined whether all system function variables have been counted (step P3). If all system function variables have not been counted, the process proceeds to step P2. When all system function variables are counted, normalization is performed for all the number of analysis units (step P4), and a vector is generated (step P5).

[Situation feature vector extraction processing]
Based on the label data assigned to the text obtained from each category, the situation in which the communication is performed is extracted as a feature. For example, consider the case where the analysis unit is one communication service. When a group of users has posted many comments on communication functions (private chat etc.) that cannot be viewed by third parties, it can be understood that private chat is important as a function that characterizes communication services. . Based on this information, a situation feature vector of the user is generated.

  FIG. 7 is a diagram showing the frequency of the labeling result of each situation feature variable. When visualizing in this way, in order to reduce the difference in the number of analysis data, normalization can be performed for all the number of analysis units. For example, for items such as S2 for which elements cannot be set in advance, the age and gender distribution of the SNS / community used by the user can be extracted in advance and registered as a pattern. For example, if the community at SNS1 is a woman and only a community in their 30s, it is registered as pattern 1, and the community at SNS2 is a male-female ratio of 7: 3, and the age distribution is 20s: 30s: 40s = 3 : 3: 4 can be registered as pattern 2 to calculate the frequency.

  FIG. 8 is a flowchart showing the situation feature vector extraction process. First, label data is acquired (step Q1), and a pattern is registered (step Q2). Next, the number of elements is counted (step Q3), and it is determined whether all the status variables have been counted (step Q4). If all the status variables are not counted, the process proceeds to step Q3. On the other hand, if all the system function variables are counted, normalization is performed with all the number of analysis units (step Q5), and the vector is changed. Generate (step Q6).

[User attitude vector extraction processing]
Based on the label data given to the text obtained from each category, what kind of attitude the text posted by the user suggests is extracted as a feature. For example, when the user has posted many comments on “THANK” and “GREET”, it can be understood that social actions are being performed on the corresponding communication service (analysis unit). Based on this information, a user attitude vector is generated.

  FIG. 9 is a diagram showing the frequency of each user attitude skill vector variable. When visualizing in this way, in order to reduce the difference in the number of analysis data, normalization can be performed for all the number of analysis units.

  FIG. 10 is a flowchart showing the user attitude vector extraction process. First, label data is acquired (step R1), and the number of elements is counted (step R2). Next, it is determined whether all user attitude variables have been counted (step R3). If all user attitude variables have not been counted, the process proceeds to step R2. When all the user attitude variables are counted, normalization is performed with all the analysis unit numbers (step R4), and a vector is generated (step R5).

  In FIG. 1, the service feature extraction / presentation module 21 performs mapping on a multidimensional space based on the feature vector created by the feature vector generation module 11. For example, it can be expressed as a plot on a two-dimensional plane by applying principal component analysis and using the first principal component and the second principal component as expression axes. In addition, three-dimensional display is possible by adding the third main component. In addition, visualization techniques such as SOM (Self-Organizing Map) can be used.

  FIG. 11 is a diagram illustrating a state in which feature vectors are grouped by clustering. A plurality of groups 101 are formed within the range of the maximum grouping frame 100. Each group 101 includes at least one feature vector 102. Further, like the feature vector 102, feature vectors having different features belong to different groups depending on the classification criteria. As shown in FIG. 11, the communication data expressed by the feature vector can be grouped and displayed by further applying a clustering method. The clustering method can be classified by, for example, the K-means method. By clicking the plot on the screen, the classification result of the communication data on each SNS can be viewed. Note that this processing can also be performed before the principal component analysis is applied.

  FIG. 12 is a diagram illustrating a state in which feature vectors are visualized hierarchically. In FIG. 12, a plurality of groups 206, 207, and 220 are formed in the highest hierarchy 201 within the range of the maximum grouping frame 200. The uppermost groups 206, 207, and 220 have second-level groups 202, 203, and 204, respectively. The second layer group 202 includes feature vectors 208 and 209, and the feature vectors 208 and 209 have third layer groups 210 and 211. The same applies to the groups 203 and 204 in the second hierarchy. That is, the second layer groups 214 and 215 have third layer groups 212 and 213, respectively. Further, the second layer groups 218 and 219 have third layer groups 216 and 217, respectively.

  As shown in FIG. 12, the service feature extraction / presentation module 21 can also hierarchically visualize feature vectors based on system feature variables, feature vectors based on situation feature variables, and feature vectors based on key words of communication data. is there. In FIG. 12, the space based on the system feature variables is the first space, but the order is variable. Further, for feature vectors based on important words of communication data, representative important words can be displayed on the space based on the result of clustering.

  FIG. 13 is a flowchart showing the operation of the service feature extraction / presentation module. First, a feature vector is extracted (step V1), and it is determined whether or not to perform dimension compression in the K-means method (step V2). If dimensional compression is not performed, the process proceeds to step V5. On the other hand, if dimensional compression is performed, principal component analysis is performed (step V3), and the Nth principal component is extracted (step V4). Next, clustering is performed (step V5), and display processing is performed to visualize the clustering result (step V6). Next, it is determined whether or not all feature vectors have been processed (step V7). If all feature vectors have not been processed, the process proceeds to step V1. On the other hand, when all the feature vectors have been processed, representative important words are extracted (step V8), and a feature vector based on the extracted important words is displayed on the space (step V9), and the process ends. .

  Next, the personal skill extraction module 27 in FIG. 1 will be described. The personal skill extraction module 27 has a system function skill extraction function. The system function skill extraction function extracts, as a skill, the degree of affinity of a text posted by a specific user with respect to a system feature based on label data assigned to the text obtained from each category. For example, if a user has posted many comments on a “SYNCHRONOUS” communication tool (such as chat), it can be understood that the user is familiar with the “SYNCHRONOUS” service. Based on this frequency information, a user's system function skill vector is generated. For example, as shown in FIG. 3, it can be expressed by the frequency of each system function characteristic variable.

  The personal skill extraction module 27 has a situation feature skill extraction function. The situation feature skill extraction function extracts, as a skill, the degree of affinity of the text posted by a specific user with respect to the situation feature based on the label data given to the text obtained from each category. For example, if a user has posted many comments on a “PUBLIC” communication tool (such as chat), it can be understood that the user is familiar with the “PUBLIC” service. Based on this frequency information, a user's situation characteristic skill vector is generated. For example, it can be calculated by using the frequency of each situation feature variable as shown in FIG.

  For S2, the age and gender distribution of the SNS / community used by the user can be extracted in advance and the pattern can be registered. For example, if the community at SNS1 is a woman and only a community in their 30s, it is registered as pattern 1, and the community at SNS2 is a male-female ratio of 7: 3, and the age distribution is 20s: 30s: 40s = 3 : 3: 4 can be registered as pattern 2 to calculate the frequency.

  The personal skill extraction module 27 has a user attitude skill extraction function. The user attitude skill extraction function extracts, as a skill, the degree of affinity of the text posted by a specific user with respect to the user attitude feature based on the label data given to the text obtained from each category. For example, if the user has posted many comments on “THANK” or “GREET”, it can be understood that the user has many experiences and skills for social comment posting. Based on this frequency information, a user attitude skill vector is generated. For example, as shown in FIG. 5, the calculation can be performed by using the frequency of each user attitude skill vector variable.

  In FIG. 1, the similarity calculation / presentation module 25 compares the feature vectors obtained by the individual skill extraction module 27 and the service feature extraction / presentation module 21 to calculate the similarity with the individual skill. The similarity can be calculated by using a cosine distance or the like. By presenting the top N cases to the user based on the degree of similarity, it is possible to recommend a service suitable for the individual skill.

  FIG. 14 is a flowchart showing similarity calculation processing. First, a personal skill vector is extracted via the personal skill extraction module 27 (step W1), and a service feature vector is extracted via the service feature extraction / presentation module 21 (step W2). Next, the similarity between the two extracted vectors is calculated (step W3), and it is determined whether or not similarities have been calculated for all SNSs (step W4). If the similarity is not calculated for all SNSs, the process proceeds to step W2. On the other hand, if similarities are listed for all SNSs, N are extracted from those with higher similarity (step W5), presented to the user (step W6), and the process ends.

  In FIG. 1, the display module 23 can calculate and visualize a partial distribution based on the system function skill vector obtained by the individual skill extraction module 27. FIG. 15 is a diagram expressing system function skill vectors. As shown in FIG. 15, for example, the frequency of “SYNCHRONISITY” of M1 can be graphed. In addition, a plurality of system characteristic variables can be displayed together. FIG. 16 shows an example in which system features from M1 to M4 are visualized together.

  The display module 23 can calculate and visualize a partial distribution based on the situation feature skill vector obtained by the individual skill extraction module 27. FIG. 17 is a diagram representing the situation feature skill vector. As shown in FIG. 17, for example, the posting frequency for the user participation mode of S1 can be graphed. In addition, a plurality of system characteristic variables can be displayed together. FIG. 18 shows an example in which system features from S1 to S2 are visualized together.

  Further, the display module 23 can calculate and visualize a partial distribution based on the user attitude skill vector obtained by the personal skill extraction module 27. FIG. 19 is a diagram expressing the user attitude skill vector. As illustrated in FIG. 19, for example, the posting frequency for the user participation mode of A1-A3 can be graphed. In addition, a plurality of system characteristic variables can be displayed together. FIG. 20 shows an example in which system features A1 to A16 are visualized together.

  Moreover, the display module 23 can visualize what skill a user uses a lot by generating a skill expression space using all the above skill vectors as a category vector for each category. As shown in FIG. 11, for example, by applying principal component analysis and using the first principal component and the second principal component as the expression axes, it can be expressed as a plot on a two-dimensional plane. By adding the third main component, three-dimensional display becomes possible. In addition, a visualization technique such as SOM can be used.

  Communication data expressed by feature vectors can also be grouped and displayed by applying a clustering method or the like. The clustering method can be classified by, for example, the K-means method. By clicking the plot on the screen, it is possible to browse the classification result and the posting frequency of the communication data on each SNS. Further, a specific SNS can be recommended to the user by using the skill distribution of the user obtained by the present system. By using the data processing system according to the present embodiment, it is possible to recommend a new SNS according to the skill of the user.

  As described above, according to the present embodiment, since the communication status can be visualized even for an SNS where it is difficult to actually acquire data, the user can perform any communication on the SNS. It is possible to know in advance whether or not Thereby, the user can easily select an SNS suitable for the user. According to the present embodiment, feature quantities (system variables, situation variables, communication variables, etc.) from a plurality of viewpoints that could not be realized by the prior art can be displayed in a unified manner.

5 Communication Data Collection Module 7 Database 9 Labeling Module 11 Feature Vector Generation Module 13 Situation Feature Skill Extraction Function 15 System Function Skill Extraction Function 17 User Attitude Skill Extraction Function 19 Key Word Extraction Function 21 Service Feature Extraction / Presentation Module 23 Display Module 25 Similar Degree calculation / presentation module 27 Individual skill extraction module

Claims (4)

A program for presenting information indicating a service corresponding to a user's operation skill,
Processing to obtain user operation data that is operated by a user and labeled based on at least one user variable;
Using the user variable of the acquired user operation data as a parameter, processing for generating an operation skill feature vector indicating the user's operation skill,
Processing for obtaining a network data feature vector generated using a network variable of network data operated on the network as a parameter;
Processing for calculating the similarity between the operation skill feature vector and the network data feature vector;
A process of selecting at least one network data feature vector as a display candidate based on the calculated similarity;
Mapping the selected network data feature vector to a multidimensional space;
A process of displaying the mapped network data feature vector on a screen, and causing a computer to execute a series of processes;
The user variable indicates a user attitude in which user operation data is handled, and generates the operation skill feature vector based on a user attitude skill vector characterized by the user attitude. Program to do. A terminal device that presents information indicating a service corresponding to a user's operation skill,
A data acquisition unit that acquires user operation data that is operated by a user and is labeled based on at least one user variable;
An operation skill feature vector generation unit that generates an operation skill feature vector indicating a user's operation skill using a user variable of the acquired user operation data as a parameter;
To obtain network data feature vector generated network variables of the network data operation on the network as a parameter, and the similarity calculation unit for calculating a similarity between the network data feature vector and the operation skill feature vectors,
Based on the degree of similarity the calculated, a mapping unit elect at least one network data feature vectors to be displayed candidates, maps the network data feature vectors the elected multidimensional space,
A display unit for displaying the mapped network data feature vector on a screen, and
The user variable indicates a user attitude in which user operation data is handled, and generates the operation skill feature vector based on a user attitude skill vector characterized by the user attitude. Terminal device to do . The terminal device according to claim 2 , further comprising a discriminator that performs labeling on input user operation data based on at least one user variable . A data processing method for presenting information indicating a service corresponding to a user's operation skill,
Obtaining user operation data operated by a user and labeled based on at least one user variable;
Generating an operation skill feature vector indicating a user's operation skill using a user variable of the acquired user operation data as a parameter;
Obtaining a network data feature vector generated using a network variable of network data operated on the network as a parameter;
Calculating a similarity between the operation skill feature vector and the network data feature vector;
Selecting at least one network data feature vector as a display candidate based on the calculated similarity; and
Mapping the selected network data feature vector to a multidimensional space;
Displaying at least the mapped network data feature vector on a screen;
The user variable indicates a user attitude in which user operation data is handled, and generates the operation skill feature vector based on a user attitude skill vector characterized by the user attitude. Data processing method.

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