Information processing device, information processing method, and information processing program

Abstract

To provide an information processing apparatus, an information processing method, and an information processing program that can improve quality of a service related to comments.SOLUTION: An information processing apparatus according to the present application comprises a classification unit and a providing unit. The classification unit classifies comments posted by a posting user for a predetermined content for each user information on the posting user. The providing unit provides comments corresponding to user information different from any one piece of user information by which the classification is performed, to a browsing user who browses comments corresponding to the any one piece of user information.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] BACKGROUND ART Conventionally, there is a service that provides comments posted by users on news articles to other users who have viewed the news articles (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-197422 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the prior art leaves room for further improvement in terms of improving the quality of service regarding comments.

[0005] The present application has been made in view of the above, and aims to provide an information processing device, an information processing method, and an information processing program that can improve the quality of services related to comments. [Means for solving the problem]

[0006] The information processing device according to the present application includes a classification unit and a provision unit. The classification unit classifies each comment posted by a posting user on a predetermined content by user information of the posting user. The provision unit provides a comment corresponding to user information different from the user information to a viewing user who has viewed the comment corresponding to any of the classified user information. [Effects of the Invention]

[0007] According to one aspect of the embodiment, it is possible to provide an effect of improving the quality of services relating to comments. [Brief explanation of the drawings]

[0008] [Figure 1A] FIG. 1A is a diagram illustrating a first process executed by the information processing device according to the embodiment. [Figure 1B] FIG. 1B is a diagram illustrating a second process executed by the information processing apparatus according to the embodiment. [Figure 1C] FIG. 1C is a diagram illustrating a third process executed by the information processing device according to the embodiment. [Figure 1D] FIG. 1D is a diagram illustrating a fourth process executed by the information processing device according to the embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of user information. [Figure 5] FIG. 5 is a diagram illustrating an example of content information. [Figure 6] FIG. 6 is a diagram illustrating an example of comment information. [Figure 7] FIG. 7 is a flowchart showing the procedure of a first process executed by the information processing apparatus according to the embodiment. [Figure 8] FIG. 8 is a flowchart showing the procedure of the second process executed by the information processing apparatus according to the embodiment. [Figure 9] FIG. 9 is a flowchart showing the procedure of the third process executed by the information processing apparatus according to the embodiment. [Figure 10] FIG. 10 is a flowchart showing the procedure of the fourth process executed by the information processing apparatus according to the embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of a hardware configuration. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an information processing device, an information processing method, and an information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to these embodiments. Furthermore, the same components in the following embodiments will be denoted by the same reference numerals, and duplicated descriptions will be omitted.

[0010] (Embodiment) First, the processes executed by the information processing device according to the embodiment will be described with reference to Fig. 1A to Fig. 1D. Fig. 1A to Fig. 1D are diagrams showing processes 1 to 4 executed by the information processing device according to the embodiment. Fig. 1A to Fig. 1D show an example of the operation of an information processing system S including an information processing device 1 according to the embodiment.

[0011] 1A to 1D, an information processing system S according to an embodiment includes an information processing device 1, a plurality of user terminals 100, and a requesting terminal 200. For ease of explanation, the requesting terminal 200 is omitted from FIGS. 1B to 1D.

[0012] In the information processing system S according to the embodiment, various processes are performed based on comments posted by users on predetermined content, thereby improving the quality of services related to comments.

[0013] First, a description will be given of process 1 shown in Fig. 1A. Specifically, the information processing device 1 first receives a request for content distribution from a requesting user via the request source terminal 200 (step S1).

[0014] The content may be, for example, information about a news article in a news distribution service or information about a product sold in a shopping service. In other words, the content is content that can accept comments from users who have distributed it. In the case of a news article, the comments are comments about the news article, and in the case of a product, the comments are reviews of the product or reviews of the store that sells the product.

[0015] Next, the information processing device 1 distributes the received content to the user via the user terminal 100 (step S2). Specifically, when the information processing device 1 receives a content distribution request from the user terminal 100, it distributes the specified content to the user terminal 100.

[0016] Next, the information processing device 1 accepts comments on the content from the user who distributed the content (step S3). The comments are, for example, text-format information, but are not limited to this and may also be audio-format information or image-format information. Note that the comment acceptance may be performed while the user is in the middle of entering the comment, or after the user has entered the comment and pressed a button (post button) to request posting.

[0017] Next, the information processing device 1 analyzes the received comments and identifies the tendency of the comment content (step S4). The tendency includes, for example, whether the comment content is favorable or unfavorable to the entire content or a part of the content, or whether the comment content supplements the entire content or a part of the content. The tendency may be set in advance or may be automatically generated based on the comment content.

[0018] The trend can be identified using, for example, a classification model in machine learning. Specifically, the information processing device 1 identifies the trend of the comments by inputting the comments into a classification model that has learned the comment contents and trends as a data set.

[0019] Next, the information processing device 1 classifies each comment according to the identified tendency (step S5). Specifically, the information processing device 1 classifies each comment according to the identified tendency.

[0020] For example, if the number of comments becomes a predetermined number or more, the information processing device 1 stops sorting comments into that trend once the number reaches the predetermined number. Also, if the number of comments becomes a predetermined number or more in a short period (a predetermined period), the information processing device 1 performs a process of thinning out unnecessary comments (for example, random extraction) from among the predetermined number or more of comments. In other words, if the predetermined number or more of comments are classified into a particular trend, the information processing device 1 considers the predetermined number or more of comments to be fraudulent comments (fake comments, abusive comments) and prevents them from increasing any further.

[0021] Next, the information processing device 1 provides various notifications to the user based on the trend of the comment content (step S6). Specifically, if the identified trend is similar (including identical) to the trend of comments that have already been posted a predetermined number of times, the information processing device 1 provides a posting prohibition notification indicating that the comment cannot be posted. The posting prohibition notification may be provided, for example, by hiding the posting button displayed on the user terminal 100 when the user is in the middle of entering a comment, or by providing a text message indicating that the posting cannot be accepted after the user has pressed the posting button. That is, the information processing device 1 performs control to prevent the posting of a comment. This can prevent, for example, a decrease in comment diversity due to a large number of comments with the same trend being posted. That is, according to the information processing device 1 according to the embodiment, the diversity of comments can be increased, thereby improving the quality of comment-related services.

[0022] Furthermore, the information processing device 1 compares the comment status for each classified trend and suggests posting a comment for another trend based on the comparison result. Specifically, the information processing device 1 compares the number of comments for each trend and suggests posting a comment for a trend in which the number of comments is less than a predetermined number. Specifically, if a predetermined number or more comments have already been collected for the trend of the posted comment, the information processing device 1 suggests posting a comment for a trend in which the number of comments is less than the predetermined number. This increases the diversity of comments, thereby improving the quality of comment-related services.

[0023] Next, the information processing device 1 determines whether the classification result obtained by the process of step S5 satisfies a predetermined condition (step S7). Specifically, the information processing device 1 determines whether a predetermined number or more of comments with trends among a plurality of pre-set trends have been posted (classified).

[0024] Next, if the classification result satisfies a predetermined condition, specifically, if a predetermined number or more of comments of a plurality of predefined tendencies have been posted, the information processing device 1 collectively provides the posted comments of each trend (step S8-1). In other words, rather than immediately providing the posted comments, comments of various tendencies are collectively provided at the timing when they are posted. This makes it possible to prevent only comments with a biased trend from being provided, and also makes it possible to collectively provide comments of various tendencies, thereby increasing the diversity of comments. In other words, the information processing device 1 according to the embodiment can improve the quality of services related to comments.

[0025] Furthermore, the information processing device 1 provides the user who posted the comment with comments that have a different trend from the trend of the comment that the user posted (step S8-2). For example, the information processing device 1 collectively provides comments with trends excluding the trend of the comment posted by the user from among the comments of each trend. In this way, the posting user can view comments with trends other than the trend of the comment posted by the posting user. In other words, the information processing device 1 according to the embodiment can improve the quality of services related to comments.

[0026] Next, the second process shown in Fig. 1B will be described. Note that in Fig. 1B, processes that overlap with the processes shown in Fig. 1A may be omitted for the sake of convenience.

[0027] 1B, the information processing device 1 collects (acquires) evaluation information indicating evaluations of each comment posted on the content (step S31). The evaluation information includes whether the comment is favorably evaluated (agrees with the comment) or negatively evaluated (does not agree with the comment).

[0028] Next, the information processing device 1 determines the diversity of evaluations based on the acquired evaluation information (step S32). For example, if the evaluations for the comment are only favorable evaluations (or only negative evaluations), the information processing device 1 determines that there is no diversity of evaluations. In other words, if the number of evaluations (number of users) for favorable evaluations and negative evaluations is balanced (if the difference in the number of evaluations is less than a predetermined value), the information processing device 1 determines that there is diversity of evaluations.

[0029] Furthermore, the information processing device 1 determines the diversity of the ratings included in the set for each combination of multiple comments. Specifically, the information processing device 1 determines whether or not there is diversity in the ratings of the comments included in the set. For example, when user A has rated comment A and comment B, if the user A has rated comment A favorably and comment B negatively, the information processing device 1 determines that there is diversity in the ratings of the set of comment A and comment B. Note that, although the diversity is determined based on user A's rating in the above, it may also be determined based on the rating of a group including multiple users with similar user information. Note that the number of comments included in the set may be three or more.

[0030] Furthermore, the information processing device 1 determines that there is diversity in evaluations for a group that includes comments with only favorable evaluations and comments with only negative evaluations. In other words, the information processing device 1 determines that there is diversity in evaluations when, when looking at the evaluations on a group-by-group basis, there is a balance between favorable evaluations and negative evaluations.

[0031] Next, the information processing device 1 determines whether or not to display the comment (whether or not to display the comment) based on the determination result (step S33). For example, the information processing device 1 determines the comment determined to have evaluation diversity as the display target. Furthermore, the information processing device 1 determines the comment of a group determined to have evaluation diversity as the display target.

[0032] Next, the information processing device 1 displays the comments (comment sets) determined as display targets on the user terminal 100 (step S34). In this way, the information processing device 1 according to the embodiment can improve the quality of comment-related services by providing comments with a variety of evaluations.

[0033] Next, the third process shown in Fig. 1C will be described. Note that in Fig. 1C, processes that overlap with the processes shown in Fig. 1A and Fig. 1B may be omitted for the sake of convenience.

[0034] In the third process shown in FIG. 1C, the information processing device 1 collects comments on the content (step S51).

[0035] Next, the information processing device 1 extracts comments whose content satisfies a predetermined content condition (step S52). For example, the information processing device 1 extracts comments whose content is inappropriate (scores equal to or greater than a predetermined value) using a model trained on feature information extracted from the comment content and scores indicating the degree of inappropriateness of the comment content as a data set.

[0036] Next, the information processing device 1 identifies users who agree with the extracted comment (step S53). Specifically, the information processing device 1 identifies users who have given favorable evaluations to the extracted comment.

[0037] Next, the information processing device 1 detects other comments whose comment content satisfies a predetermined condition based on the agreement status of the identified user (specific user) with other comments (step S54). For example, the information processing device 1 detects other comments for which a predetermined number of specific users have agreed with the other comments as other comments that satisfy the predetermined condition. In other words, the information processing device 1 detects other comments for which a large number of specific users have agreed with the other comments determined in step S52 to be not inappropriate as comments whose comment content is inappropriate. This makes it possible to find other inappropriate comments based on the agreement status of users who have agreed with the inappropriate comment, thereby improving the quality of comment-related services.

[0038] Next, the information processing device 1 displays the comments on the user terminal 100, excluding the other comments detected in step S54 and the comments extracted in step S52 from the comments to be displayed (step S55).

[0039] Next, the fourth process shown in Fig. 1D will be described. Note that in Fig. 1D, processes that overlap with the processes shown in Figs. 1A to 1C may be omitted for the sake of convenience.

[0040] 1D, the information processing device 1 distributes content to the user terminal 100 (step S71). Subsequently, the information processing device 1 accepts comments on the content from the user (step S72).

[0041] Next, the information processing device 1 classifies each of the received comments according to the tendency of the comment content or according to the user information of the posting user (step S73).

[0042] Next, the information processing device 1 identifies user information corresponding to the comments viewed by the viewing user or a trend of the comments (step S74). Specifically, the information processing device 1 identifies user information or a trend into which comments previously viewed by the viewing user are classified.

[0043] Next, the information processing device 1 extracts comments corresponding to user information different from the identified user information or comments with a tendency different from the identified tendency (step S75).

[0044] Next, the information processing device 1 provides the extracted comments to the user terminal 100 of the viewing user (step S76).

[0045] This allows the viewing user to be provided with comments that have different trends and user information from the comments that they have viewed, thereby improving the quality of the service related to comments.

[0046] Next, a configuration example of an information processing system S according to an embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing a configuration example of the information processing system S according to an embodiment. As shown in Fig. 2, in the information processing system S according to an embodiment, an information processing device 1, a plurality of user terminals 100, and a plurality of request source terminals 200 are connected to a network N by wire or wirelessly. The network N is, for example, a network such as the Internet, a WAN (Wide Area Network), or a LAN (Local Area Network).

[0047] The information processing device 1 is a server device that executes the information processing method according to the embodiment. The information processing device 1 executes the processes 1 to 4 described above with reference to FIGS. 1A to 1D.

[0048] In addition, the information processing device 1 is an information processing device that cooperates with multiple user terminals 100 and multiple requesting terminals 200, and provides each user terminal 100 and each requesting terminal 200 with API (Application Programming Interface) services for various applications (hereinafter referred to as apps), etc., and various data, and is realized by a server device, a cloud system, etc.

[0049] Furthermore, the information processing device 1 may be an information processing device that provides some kind of online web service to each user terminal 100 and each requesting terminal 200. For example, the information processing device 1 may provide services such as internet connection, search service, SNS (Social Networking Service), electronic commerce (EC), electronic payment, online games, online banking, online trading, hotel and ticket reservations, video and music distribution, news, maps, route search, route guidance, line information, operation information, and weather forecasts as web services. In practice, the information processing device 1 may cooperate with various servers that provide the above-mentioned web services and act as an intermediary for the web services or may be responsible for processing the web services.

[0050] The user terminal 100 is a terminal device carried by a user who views content, comments, ratings on comments, etc. The user terminal 100 can be any type of terminal device, such as a smartphone, a desktop PC, a notebook PC, or a tablet PC. The user terminal 100 transmits various types of information to the information processing device 1, etc., and receives information provided by the information processing device 1, etc.

[0051] The requesting terminal 200 is a terminal device owned by a requesting user who requests content distribution. The requesting terminal 200 can be any type of terminal device, such as a smartphone, a desktop PC, a notebook PC, or a tablet PC. The requesting terminal 200 transmits various types of information to the information processing device 1 or the like, and receives information provided by the information processing device 1 or the like.

[0052] Next, an example of the configuration of the information processing device 1 will be described with reference to FIG.

[0053] Fig. 3 is a diagram illustrating an example of the configuration of an information processing device 1 according to an embodiment. As illustrated in Fig. 3, the information processing device 1 includes a communication unit 2, a control unit 3, and a storage unit 4. The control unit 3 includes a distribution unit 31, an acquisition unit 32, an identification unit 33, a classification unit 34, a determination unit 35, a determination unit 36, a detection unit 37, a reception control unit 38, a suggestion unit 39, and a provision unit 40. The storage unit 4 stores user information 41, content information 42, and comment information 43.

[0054] The communication unit 2 is realized by, for example, a network interface card (NIC), etc. The communication unit 2 is connected to a network by wire or wirelessly.

[0055] The control unit 3 is a controller, and is realized by a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the information processing device 1 using a RAM or the like as a work area. The control unit 3 is also a controller, and may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a GPGPU (General Purpose Graphic Processing Unit).

[0056] The storage unit 4 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk.

[0057] The user information 41 is information relating to a user. Fig. 4 is a diagram showing an example of the user information 41. As shown in Fig. 4, the user information 41 includes items such as "user ID" and "user information."

[0058] "User ID" is identification information that identifies a user. "User information" is information about a user. "User information" includes, for example, attribute information about the user's attributes and behavioral information. Attribute information includes psychographic attributes and demographic attributes. Behavioral information includes the user's behavior on the network (search behavior, purchasing behavior, comment posting behavior, rating posting behavior, etc.).

[0059] Next, content information 42 is information related to content. Fig. 5 is a diagram showing an example of content information 42. As shown in Fig. 5, content information 42 includes items such as "content ID," "type," "details information," and "comment information."

[0060] "Content ID" is identification information that identifies the content. "Type" is information that indicates the type of content. "Content information" is information that indicates the content, and includes text information, image information, etc. "Comment information" is information on comments posted to the content, and the "Comment ID" of comment information 43 described below is entered.

[0061] Next, comment information 43 is information related to comments posted on content. Fig. 6 is a diagram showing an example of comment information 43. As shown in Fig. 6, comment information 43 includes items such as "comment ID," "trend," "content information," and "evaluation information."

[0062] "Comment ID" is identification information that identifies a comment. "Trend" is information that indicates the trend of the comment content. "Content information" is information that indicates the comment content and includes text information. "Rating information" is information about the ratings users have given to the comment, and includes, for example, information about the number of users who have given favorable ratings and the number of users who have given negative ratings.

[0063] Next, we will explain each function of the control unit 3 of the information processing device 1 (distribution unit 31, acquisition unit 32, identification unit 33, classification unit 34, judgment unit 35, decision unit 36, detection unit 37, reception control unit 38, proposal unit 39 and provision unit 40).

[0064] The distribution unit 31 distributes content to the user terminal 100. The distribution unit 31 acquires content to be distributed from the requesting user via the request source terminal 200 and stores the content in the content information 42. Then, when the distribution unit 31 receives a content distribution request from the user via the user terminal 100, it distributes the target content to the user terminal 100.

[0065] Additionally, the distribution unit 31 distributes, together with the content, comments posted by other users who have viewed the content, and evaluations posted by other users on the comments.

[0066] The acquisition unit 32 acquires comments on the content from the user who distributed the content. The acquisition unit 32 may acquire the comments at any timing. For example, the acquisition unit 32 acquires the comment being input when a predetermined condition is met while the comment is being input (the number of characters is a predetermined number or more, the elapsed time since input is a predetermined time or more, etc.). The acquisition unit 32 also acquires the comment when the above-mentioned post button is pressed after the comment is input. The acquisition unit 32 also acquires evaluation information indicating an evaluation of the comment.

[0067] The identification unit 33 identifies a trend in the comment content based on the comment content. The trend can be identified using, for example, a classification model in machine learning. Specifically, the identification unit 33 identifies the trend of the comment by inputting the comment into a classification model that has learned the comment content and trends as a data set.

[0068] The identification unit 33 also identifies user information of the user who posted the comment. The identified user information is, for example, attribute information such as gender, location, and occupation, and behavior information such as search behavior and purchasing behavior.

[0069] Furthermore, the identification unit 33 identifies users who agree with a comment whose content is determined to satisfy a predetermined content condition by the determination unit 35, which will be described later. For example, the identification unit 33 identifies users who have given a favorable evaluation to a comment that satisfies the predetermined content condition.

[0070] Furthermore, the identification unit 33 identifies a tendency corresponding to a posting user based on the comment-related behavior history of the posting user who posted the comment. The comment-related behavior history is, for example, a comment posting history of past comments, a history of evaluations of comments posted by other posting users, etc.

[0071] For example, the identification unit 33 identifies a trend corresponding to a posting user based on the trend of comments posted by the posting user in the past. Specifically, the identification unit 33 tallies the trends of comments posted in the past, and identifies the trend with the largest number of comments as the trend corresponding to the posting user. Furthermore, the identification unit 33 may identify, based on the above-mentioned tallied results, multiple trends with the number of comments equal to or greater than a predetermined number as trends (multiple) corresponding to the posting user.

[0072] Furthermore, the identification unit 33 identifies a tendency corresponding to the posting user based on the content of the evaluation history for comments posted by other posting users. Specifically, when a posting user has given a favorable evaluation to a comment posted by another posting user, the identification unit 33 identifies the tendency of the evaluated comment as a tendency corresponding to the posting user.

[0073] Furthermore, when a posting user negatively evaluates a comment posted by another posting user, the identification unit 33 identifies a tendency different from the tendency of the evaluated comment as a tendency corresponding to the posting user.

[0074] As a method for selecting a trend different from the trend of the rated comment, it is possible to select based on the distance between trends in the distributed representation space. That is, the identification unit 33 identifies a trend that is a predetermined distance or more away from the trend of the rated comment in the distributed representation space as the trend corresponding to the posting user.

[0075] The classification unit 34 classifies each comment for each identified trend or for each identified user information. Specifically, the classification unit 34 sorts each comment for each identified trend or for each identified user information. More specifically, the classification unit 34 classifies the identified trend of the comment into one of multiple trends set in advance. Such classification is possible by inputting the identified trend and the trend to which the identified trend belongs from among the multiple trends set in advance as a data set into a model that has been trained.

[0076] For example, for trends or user information for which the number of comments exceeds a predetermined number, the classification unit 34 stops sorting comments for such trends or user information once the number exceeds the predetermined number. Furthermore, for trends or user information for which a predetermined number or more comments have been classified in a short period (a predetermined period), the classification unit 34 performs a process of thinning out unnecessary comments (e.g., random extraction) from among the predetermined number or more of comments. In other words, when a predetermined number or more of comments have been classified into a particular trend or user information, the classification unit 34 considers the predetermined number or more of comments to be fraudulent comments (fake comments, abusive comments) and prevents them from increasing any further.

[0077] Furthermore, the classification unit 34 may reset the multiple trends to be classified based on the classification results. Specifically, when the number of comments classified into a specific trend is significantly large, in other words, when the number of comments classified into a specific trend is equal to or greater than a predetermined number and the number of comments classified into other trends is less than a predetermined number, the classification unit 34 may analyze the content of the classified comments and subdivide the specific trend into multiple trends. Alternatively, the classification unit 34 may reduce the number of other trends by combining other trends with fewer than a predetermined number of comments.

[0078] Such a method of reconstructing trends can be realized, for example, by creating a distributed representation of each comment based on the comment content, and automatically generating an axis that separates areas in the distributed representation space as a trend.

[0079] The determination unit 35 performs various determination processes. For example, the determination unit 35 determines whether the trend of the identified comment satisfies a condition based on the trend of already posted comments. Specifically, the determination unit 35 determines that the above condition is satisfied when the trend of the identified comment is similar to the trend into which a predetermined number of already posted comments are classified.

[0080] In addition, the judgment unit 35 calculates the ratio of the number of posted comments for each trend to the number of posted comments for all trends, and if such ratio is equal to or greater than a predetermined value and is similar to the trend of the identified comment, it judges that the above condition is met.

[0081] Furthermore, the determination unit 35 may determine whether the above conditions are met by taking into account the relationship between the posting user and the tendencies of comments posted by the posting user in the past. Specifically, the determination unit 35 first estimates a risk indicating the likelihood of posting for each trend, depending on the number of comments for each trend posted by the posting user in the past. The risk is set higher for a trend with a larger number of comments. Then, the determination unit 35 calculates a score indicating the degree of similarity between the identified comment trend and the tendencies into which a predetermined number of posted comments are categorized, and determines whether the above conditions are met based on the score and the risk.

[0082] Furthermore, the determination unit 35 determines the diversity of the evaluations based on the evaluation information acquired by the acquisition unit 32. For example, the determination unit 35 determines that there is no diversity of evaluations when the evaluations on the comment are only favorable evaluations (or only negative evaluations). In other words, the determination unit 35 determines that there is diversity of evaluations when the number of evaluations (number of users) of favorable evaluations and negative evaluations are balanced (when the difference in the number of evaluations is less than a predetermined value).

[0083] Furthermore, the determination unit 35 may determine the diversity of the ratings in the multiple comments. For example, the determination unit 35 determines the diversity of the ratings included in each set for each combination of multiple comments. Specifically, the determination unit 35 determines whether or not there is diversity in the ratings of the comments included in the set. For example, when user A has rated comment A and comment B, if the user A has rated comment A favorably and comment B negatively, the determination unit 35 determines that there is diversity in the ratings of the set of comment A and comment B. Note that, although the diversity is determined based on user A's rating in the above example, the diversity may also be determined based on the rating of a group including multiple users with similar user information. Note that the number of comments included in the set may be three or more.

[0084] Furthermore, the determination unit 35 determines that there is diversity in evaluations for a group that includes comments with only favorable evaluations and comments with only negative evaluations. In other words, the determination unit 35 determines that there is diversity in evaluations when, when looking at the evaluations on a group-by-group basis, there is a balance between favorable evaluations and negative evaluations.

[0085] The determination unit 35 may also determine the diversity of users who have made ratings. For example, the determination unit 35 determines that there is diversity in ratings when the attribute information of the users who have made favorable ratings (or negative ratings) on a comment is not similar, that is, when users with various attributes have made favorable ratings.

[0086] On the other hand, if the attribute information of each user who gave a favorable rating (or a negative rating) to a comment is similar, that is, if users with similar attributes gave favorable ratings, the judgment unit 35 judges that there is no diversity in the ratings.

[0087] Furthermore, the determination unit 35 may group users based on user information and determine the diversity of evaluations for each group. For example, the determination unit 35 determines that there is diversity of evaluations when the information (e.g., group attribute information) of groups that have given favorable evaluations (or negative evaluations) to a comment is not similar, that is, when groups with various attributes have given favorable evaluations.

[0088] On the other hand, if the information of each group that gave a favorable (or negative) evaluation to a comment is similar, i.e., if groups with similar attributes gave favorable evaluations, the judgment unit 35 judges that there is no diversity in the evaluations.

[0089] The determination unit 35 also determines whether the comment content satisfies a predetermined content condition. Specifically, the determination unit 35 inputs the comment into a model trained on a dataset of feature information extracted from the comment content and a score indicating the degree of inappropriateness of the comment content, and determines that the comment satisfies the content condition if the score output from the model is equal to or greater than a predetermined value. In other words, the content condition is a condition for determining whether the comment content is inappropriate (violent, obscene, defamatory, etc.). The feature information also includes whether the comment content contains sentences of violent, obscene, or defamatory language, as well as the presence or absence of words related to these expressions, and the number of such sentences and words. The determination unit 35 may correct the model based on other comments that satisfy the predetermined condition (inappropriate) detected by the detection unit 37, which will be described later.

[0090] The determination unit 36 ​​determines whether to display a comment based on the determination result of the determination unit 35. Specifically, the determination unit 36 ​​determines whether to display a comment based on the determination result of the diversity of evaluations. Specifically, the determination unit 36 ​​determines a comment determined to have diversity of evaluations as a display target. Furthermore, the determination unit 36 ​​determines a comment of a group determined to have diversity of evaluations as a display target.

[0091] Furthermore, the determination unit 36 ​​determines the display order of the comments based on the determination result of the diversity of the evaluations. For example, the determination unit 36 ​​displays comments determined to have diversity of evaluations at the top of a plurality of comments for which a large number of users have given favorable (or negative) evaluations.

[0092] The detection unit 37 detects other comments whose content satisfies a predetermined condition based on the agreement status of the users identified by the identification unit 33 (users who agree with the comment satisfying the content condition) with other comments. Specifically, the detection unit 37 detects other comments, among the identified users, whose number of users who agree with the other comment is a predetermined number or more, as other comments that satisfy the predetermined condition. Alternatively, the detection unit 37 may detect other comments, for which the ratio of the number of users identified by the identification unit 33 to the number of all users who agree with the other comment is a predetermined value or more, as other comments that satisfy the predetermined condition. In other words, the detection unit 37 detects other comments whose content is determined by the determination unit 35 to be not inappropriate, that are agreed with by a majority of the identified users, as comments whose content is inappropriate. Note that the other comments may be comments posted on the same content (e.g., the same news article) as the comment that satisfies the predetermined content condition, or may be comments posted on other content of the same type (e.g., other news articles). Furthermore, the detection unit 37 may detect appropriate comments from among the comments determined to be inappropriate based on the agreement state of the identified user with other comments. Furthermore, the other comments detected by the detection unit 37 may be, in addition to inappropriate comments, comments seeking sympathy from other users, comments posted by the parties involved in the content, etc.

[0093] The detection unit 37 may also detect other comments that satisfy a predetermined condition by taking into account the identified user's context information. For example, if the user's context information when agreeing with an inappropriate comment is similar to the user's context information when agreeing with another comment, the detection unit 37 detects the other comment as a comment that satisfies a predetermined condition. That is, the detection unit 37 estimates from the user's context information whether the user is in a mood to agree with the inappropriate comment (a contrarian mood), and detects other comments agreed with when the user is in a mood to agree with the inappropriate comment as other (inappropriate) comments that satisfy a predetermined condition. Note that the context information includes search context, such as a search query, and context related to the user's emotions. The context related to the user's emotions can be inferred from, for example, the written expression in an email or the conversational expression in a voice conversation.

[0094] The reception control unit 38 performs control to prevent the reception of comment postings based on the determination result of the determination unit 35. Specifically, if the identified tendency is similar (including the same) as the tendency of comments that have already been posted a predetermined number of times or more, the reception control unit 38 issues a posting prohibition notice indicating that the comment cannot be posted. The posting prohibition notice may be issued by hiding the post button displayed on the user terminal 100 if the user is in the middle of entering a comment, or may be issued by sending a text message indicating that the post cannot be accepted if the user has already pressed the post button.

[0095] Furthermore, the reception control unit 38 may suggest changing the trend of the input comments as a control to prevent the posting of comments from being accepted. In this case, the reception control unit 38 suggests changing the trend to one where the number of classified comments is less than a predetermined number, based on the classification results of the classification unit 34.

[0096] The suggestion unit 39 compares the comment status for each trend classified by the classification unit 34 and suggests posting a comment based on the comparison result. The suggestion to post a comment may be made, for example, when viewing content or when entering a comment.

[0097] For example, the suggestion unit 39 compares the number of comments for each trend and suggests posting comments for which the number of comments tends to be less than a predetermined number. The suggestion unit 39 also compares the ratio of the number of comments for each trend to the total number of comments, which is the sum of the number of comments for each trend, and suggests posting comments for which the ratio tends to be less than a predetermined value. Alternatively, the suggestion unit 39 suggests posting comments for which the ratio tends to be low when the difference in the ratio between the trends is equal to or greater than a predetermined value.

[0098] Furthermore, if the number of trends is less than a predetermined number, the suggestion unit 39 suggests posting a comment of a new trend. Furthermore, when suggesting posting a comment of a specific trend, the suggestion unit 39 suggests auxiliary information that assists in posting a comment of the specific trend, based on previously posted comments of the specific trend. The auxiliary information is, for example, summary data of the posted comments, or list data that lists words related to a specific trend extracted from the previously posted comments.

[0099] Furthermore, when the number of comments for each trend is balanced and there is a trend in which the number of comments whose content meets predetermined content conditions is equal to or greater than a predetermined number, the suggestion unit 39 suggests posting a comment for that trend. In other words, when a certain number of comments for each trend has been secured but there are many comments with inappropriate content for a particular trend, the suggestion unit 39 suggests posting a comment for that particular trend. Specifically, when a predetermined number or more comments have already been collected for the trend of the posted comment, the suggestion unit 39 suggests posting a comment for a trend in which the number of comments is less than the predetermined number.

[0100] The providing unit 40 provides various information to the user. For example, when the classification results by the classification unit 34 satisfy a predetermined condition, the providing unit 40 provides comments of each trend. Specifically, the providing unit 40 determines that the predetermined condition is satisfied when a predetermined number or more of the trends into which the comments are classified are included among a plurality of predefined trends, and provides the posted comments of each trend in a group. After providing the comments in a group, the providing unit 40 resets the classification results, reclassifies newly posted comments, and provides the newly posted comments of each trend in a group when the above-mentioned predetermined condition is satisfied. In other words, the providing unit 40 provides the comments of each trend in a group each time the classification results satisfy the predetermined condition.

[0101] The comments to be provided may be all comments for each trend, or for a trend in which there are a predetermined number or more of comments, one or more representative comments may be selected from the predetermined number or more of comments. The providing unit 40 may select the comments based on the posting history of the posting user, for example, by giving priority to comments from a posting user who has posted a large number of comments in the past, or may simply select comments at random.

[0102] Furthermore, the providing unit 40 may select a representative comment based on the similarity of attribute information between the posting user and the viewing user. Specifically, the providing unit 40 selects a comment posted by a posting user that has a high similarity to the attribute information of the viewing user as the representative comment.

[0103] Furthermore, the providing unit 40 provides a comment having a different tendency to a viewing user who has viewed a comment having one of the tendencies classified by the classification unit 34. Furthermore, the providing unit 40 provides a comment having a different tendency to a viewing user who has viewed a comment corresponding to one of the user information classified by the classification unit 34.

[0104] For example, the providing unit 40 creates a distributed representation of each trend (or each piece of user information) and provides a comment of the trend (or user information) that is the furthest from the trend (or user information) of the comment viewed by the viewing user in the distributed representation space. The providing unit 40 may also provide comments of multiple trends (or user information) that are a predetermined distance or more away from the trend (or user information) of the comment viewed by the viewing user. The providing unit 40 may also select a representative comment by the above method when the number of comments of the trend to be provided is a predetermined number or more.

[0105] Furthermore, the providing unit 40 provides the posting user who posted the comment with a comment that has a tendency different from the tendency of the comment that the posting user posted. In other words, the providing unit 40 provides the posting user with a comment that has a tendency different from the tendency corresponding to the posting user identified by the identifying unit 33 based on the behavioral history of the posting user.

[0106] For example, the providing unit 40 hides comments that tend to correspond to the posting user and displays comments that tend to differ from the posting user. Furthermore, the providing unit 40 arranges comments that tend to differ from the posting user in the display order higher than the comments that tend to correspond to the posting user.

[0107] Furthermore, the providing unit 40 displays comments with a tendency corresponding to the posting user and comments with a tendency different from the corresponding tendency in different display modes, such as background color, character color, character underline, character font, character size, etc.

[0108] Furthermore, the providing unit 40 provides information on other comments that satisfy the predetermined condition (that are inappropriate) detected by the detecting unit 37 to, for example, an administrator who manages the content (or comments).

[0109] Next, the processing procedures of the first to fourth processes executed by the information processing device 1 according to the embodiment will be described with reference to Fig. 7 to Fig. 10. Fig. 7 to Fig. 10 are flowcharts showing the processing procedures of the first to fourth processes executed by the information processing device 1 according to the embodiment.

[0110] First, the process 1 will be described with reference to Fig. 7. As shown in Fig. 7, the control unit 3 first distributes content to the user terminal 100 (step S101).

[0111] Next, the control unit 3 accepts comments from users regarding the distributed content (step S102).

[0112] Next, the control unit 3 identifies the tendency of the comment contents (step S103).

[0113] Next, the control unit 3 classifies the comments according to the identified tendencies (step S104).

[0114] Next, the control unit 3 determines whether the identified tendency is similar to the tendency of the posted comments that have been posted a predetermined number of times or more (step S105).

[0115] If the identified tendency is not similar to the tendency of the posted comments that have been posted a predetermined number of times or more (step S105: No), the control unit 3 determines whether the classification result satisfies a predetermined condition (step S106).

[0116] If the classification result satisfies a predetermined condition (step S106: Yes), the control unit 3 collectively provides the comments for each trend to the user (step S107), and ends the process.

[0117] On the other hand, if the classification result does not satisfy the predetermined condition (step S106: No), the control unit 3 ends the process without providing a comment.

[0118] Also, in step S105, if the identified trend is similar to the trend of comments that have been posted a predetermined number of times or more (step S105: Yes), the control unit 3 notifies the user that the comment cannot be posted or suggests that the user post a comment with a different trend (step S108), and ends the processing.

[0119] Next, the process 2 will be described with reference to Fig. 8. As shown in Fig. 8, the control unit 3 collects evaluation information indicating evaluations of comments (step S201).

[0120] Next, the control unit 3 determines the diversity of the evaluations based on the collected evaluation information (step S202).

[0121] Next, the control unit 3 determines the comments to be displayed based on the determination result (step S203).

[0122] Next, the control unit 3 provides the determined comment to the user (step S204), and ends the process.

[0123] Next, the third process will be described with reference to Fig. 9. As shown in Fig. 9, the control unit 3 collects each user's comment on the content (step S301).

[0124] Next, the control unit 3 extracts comments whose content satisfies a predetermined content condition (step S302).

[0125] Next, the control unit 3 identifies users who agree with the extracted comment (step S303).

[0126] Next, the control unit 3 detects other comments based on the agreement state of the identified user with other comments (step S304), and ends the process.

[0127] Next, the fourth process will be described with reference to Fig. 10. As shown in Fig. 10, the control unit 3 classifies each comment according to the tendency of the comment content (or according to the user information) (step S401).

[0128] Next, the control unit 3 identifies the tendency of the comments viewed by the viewing user (user information) (step S402).

[0129] Next, the control unit 3 extracts comments with a different tendency (or user information) from the identified tendency (or user information) (step S403).

[0130] Next, the control unit 3 provides the extracted comments to the user (step S404), and ends the process.

[0131] 〔others〕 Furthermore, among the processes described in the above embodiments, some of the processes described as being performed automatically can also be performed manually. Alternatively, all or some of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0132] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0133] 3 may be held in a storage server or the like, rather than being held by each device. In this case, each device obtains various pieces of information by accessing the storage server.

[0134] [Hardware configuration] The information processing device 1 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in Fig. 11, for example. Fig. 11 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which an arithmetic unit 1030, a primary storage device 1040, a secondary storage device 1050, an output IF (Interface) 1060, an input IF 1070, and a network IF 1080 are connected via a bus 1090.

[0135] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The primary storage device 1040 is a memory device, such as a RAM, that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used by the arithmetic device 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), an HDD (Hard Disk Drive), a flash memory, or the like.

[0136] The output IF 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a monitor or a printer, and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (High Definition Multimedia Interface), etc. The input IF 1070 is an interface for receiving information from various input devices 1020, such as a mouse, keyboard, scanner, etc., and is realized by a USB, etc.

[0137] The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. The input device 1020 may also be an external storage medium such as a USB memory.

[0138] The network IF 1080 receives data from other devices via the network N and sends it to the arithmetic device 1030, and also transmits data generated by the arithmetic device 1030 to other devices via the network N.

[0139] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0140] For example, when the computer 1000 functions as the information processing device 1, the arithmetic unit 1030 of the computer 1000 executes a program loaded onto the primary storage device 1040, thereby realizing the functions of the control unit 3.

[0141] 〔effect〕 As described above, the information processing device 1 according to the embodiment includes the classification unit 34 and the providing unit 40. The classification unit 34 classifies each comment posted on a predetermined content according to the tendency of the comment content. The providing unit 40 provides the comments of each tendency when the classification result satisfies a predetermined condition.

[0142] The information processing device 1 according to the embodiment also includes an identification unit 33, a determination unit 35, and a reception control unit 38. The identification unit 33 identifies a tendency of the content of comments entered by users that are posted on predetermined content. The determination unit 35 determines whether the tendency of the identified comment satisfies a condition based on the tendency of comments that have already been posted. The reception control unit 38 performs control to avoid accepting the posting of a comment based on the determination result.

[0143] The information processing device 1 according to the embodiment also includes an acquisition unit 32, a determination unit 35, and a decision unit 36. The acquisition unit 32 acquires rating information indicating ratings of comments posted on predetermined content. The determination unit 35 determines the diversity of ratings based on the acquired rating information. The decision unit 36 ​​determines whether to display the comments based on the determination result.

[0144] The information processing device 1 according to the embodiment also includes a classification unit 34 and a provision unit 40. The classification unit 34 classifies each comment posted on a predetermined content according to the tendency of the comment content. The provision unit 40 provides a viewing user who has viewed a comment with one of the classified tendencies with a comment with a different tendency from the comment with the classified tendency.

[0145] The information processing device 1 according to the embodiment also includes a classification unit 34 and a provision unit 40. The classification unit 34 classifies each comment posted by a posting user on a predetermined piece of content according to the posting user's user information. The provision unit 40 provides a viewing user who has viewed a comment corresponding to any of the classified user information with a comment corresponding to user information different from the user information.

[0146] The information processing device 1 according to the embodiment also includes a classification unit 34, an identification unit 33, and a provision unit 40. The classification unit 34 classifies each comment posted on a predetermined content according to the tendency of the comment content. The identification unit 33 identifies a tendency corresponding to the user from among the classified tendencies based on the user's behavior history regarding comments. The provision unit 40 provides the user with comments with a tendency different from the identified tendency.

[0147] The information processing device 1 according to the embodiment also includes an identification unit 33 and a detection unit 37. The identification unit 33 identifies users who agree with a comment whose content satisfies a predetermined content condition. The detection unit 37 detects other comments whose content satisfies the predetermined condition based on the agreement status of the identified users with other comments.

[0148] The information processing device 1 according to the embodiment also includes a classification unit 34 and a suggestion unit 39. The classification unit 34 classifies each comment posted on a predetermined piece of content according to the tendency of the comment content. The suggestion unit 39 compares the comment status for each classified tendency and suggests posting a comment based on the comparison result.

[0149] According to the information processing device 1 according to each of the above-described embodiments, it is possible to improve the quality of services relating to comments.

[0150] Although some of the embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have undergone various modifications and improvements based on the knowledge of those skilled in the art.

[0151] 〔others〕 Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0152] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0153] Furthermore, the processes described in the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the process contents.

[0154] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, the control unit 3 can be read as control means or a control circuit. [Explanation of symbols]

[0155] 1. Information processing equipment 2. Communications Department 3. Control Unit 4 Storage section 31 Distribution Department 32 Acquisition Department 33 Specific part 34 Classification Department 35 Judgment section 36 Decision Section 37 Detector 38 Reception Control Unit 39 Proposal Department 40 Providing Department 41 User Information 42 Content Information 43 Comment Information 100 user terminals 200 Requesting device S Information Processing System

Claims

1. a classification unit that classifies each comment posted by a posting user on a predetermined content according to user information of the posting user; a storage unit that assigns a comment ID to each of the comments and stores comment information linked to the user information that is the classification result; a providing unit that provides, based on the comment information, to a user who has previously provided the comment corresponding to predetermined user information, the comment corresponding to the user information different from the user information; Equipped with The user information is The information includes at least one of attribute information and behavior information, The classification unit The comments are classified based on at least one of attribute information and behavior information. Information processing device.

2. The classification unit Each comment is classified into one of multiple pre-defined user information categories, The providing unit Providing the comment posted by a user of user information different from the user information of the comment provided to the user among the plurality of user information. The information processing device according to claim 1 .

3. The classification unit Each comment is converted into a distributed representation based on the comment content, and the axis that divides the area of ​​the distributed representation space is set as the user information. The information processing device according to claim 1 .

4. The providing unit Each user's information is distributed, and in the distributed representation space, a comment of the user information that is the furthest from the user information of the comment provided to the user is provided. The information processing device according to claim 1 .

5. The providing unit Each user information is made into a distributed representation, and in the distributed representation space, comments of a plurality of user information that are at least a predetermined distance from the user information of the comment provided to the user are provided. The information processing device according to claim 1 .

6. 1. A computer-implemented information processing method, comprising: a classification step of classifying each comment posted by a posting user on a predetermined content according to user information of the posting user; a storage step of assigning a comment ID to each of the comments and storing comment information linked to the user information, which is the classification result, in a storage unit; a providing step of providing, based on the comment information, a comment corresponding to user information different from the user information provided in the past to a user who provided the comment corresponding to the predetermined user information; Including, The user information is The information includes at least one of attribute information and behavior information, The classification step includes: The comments are classified based on at least one of attribute information and behavior information. Information processing methods.

7. a classification step of classifying each comment posted by a posting user on a predetermined content according to user information of the posting user; a storage step of assigning a comment ID to each of the comments and storing comment information linked to the user information, which is the classification result, in a storage unit; a provision step of providing, based on the comment information, a comment corresponding to user information different from the user information provided in the past to a user who provided the comment corresponding to the predetermined user information; on the computer, The user information is The information includes at least one of attribute information and behavior information, The classification procedure comprises: The comments are classified based on at least one of attribute information and behavior information. Information processing program.

Images