JP7757249B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, there are services that provide comments posted by users on news articles to other users who have viewed the news articles (see, for example, Patent Document 1).

Japanese Patent Application Laid-Open No. 2019-197422

However, conventional technology leaves room for further improvement in terms of improving the quality of service related to comments.

This application has been made in light of the above, and aims to provide an information processing device, information processing method, and information processing program that can improve the quality of services related to comments.

The information processing device according to the present application includes a classification unit and a provision unit. The classification unit classifies each comment posted to a predetermined piece of content according to the tendency of the comment content. The provision unit provides a comment with a different tendency to a viewing user who has viewed a comment with one of the classified tendencies.

According to one aspect of the embodiment, it is possible to improve the quality of services related to comments.

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

Below, detailed explanations will be given of the information processing device, information processing method, and information processing program (hereinafter referred to as "embodiments") according to the present application, with reference to the drawings. Note that the information processing device, information processing method, and information processing program according to the present application are not limited to these embodiments. Furthermore, identical components in the following embodiments will be designated by the same reference numerals, and duplicate explanations will be omitted.

(Embodiment)
First, the processing executed by the information processing device according to the embodiment will be described using Figures 1A to 1D. Figures 1A to 1D are diagrams showing processes 1 to 4 executed by the information processing device according to the embodiment. Note that Figures 1A to 1D show an example of the operation of an information processing system S including an information processing device 1 according to the embodiment.

As shown in Figures 1A to 1D, an information processing system S according to an embodiment includes an information processing device 1, multiple user terminals 100, and a requesting terminal 200. Note that for ease of explanation, the requesting terminal 200 is omitted from Figures 1B to 1D.

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

First, we will explain process 1 shown in Figure 1A. Specifically, the information processing device 1 first accepts a content distribution request from a requesting user via the requesting terminal 200 (step S1).

Content is, for example, information about news articles in a news distribution service, or information about products for sale in a shopping service. In other words, 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, they are reviews of the product or reviews of the store that sells the product.

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.

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 or image-format information. Note that comments may be accepted while the user is in the middle of entering them, or after the user has entered them and pressed a button (post button) to request posting.

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

To identify trends, for example, a classification model in machine learning can be used. Specifically, the information processing device 1 identifies comment trends by inputting comments into a classification model that has learned the comment content and trends as a dataset.

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

For example, if the number of comments for a trend exceeds a predetermined number, the information processing device 1 will stop sorting comments into that trend once the number exceeds the predetermined number. Furthermore, if the trend is such that a predetermined number or more comments have been classified in a short period of time (a predetermined period), the information processing device 1 will perform a process of thinning out unnecessary comments (for example, by random extraction) from among the comments exceeding the predetermined number. In other words, if a predetermined number or more comments have been classified into a particular trend, the information processing device 1 will consider these comments exceeding the predetermined number to be fraudulent comments (fake comments, abusive comments) and will prevent them from increasing any further.

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 post-disabled notification indicating that the comment cannot be posted. The post-disabled notification may be provided, for example, by hiding the post button displayed on the user terminal 100 if the user is in the middle of entering a comment, or by sending a text message indicating that the post cannot be accepted if the user has already pressed the post button. In other words, the information processing device 1 performs control to prevent the posting of comments. This can prevent, for example, a decrease in comment diversity due to a large number of comments with the same trend being posted. In other words, the information processing device 1 according to the embodiment can increase the diversity of comments, thereby improving the quality of comment-related services.

The information processing device 1 also compares the comment status for each classified trend and suggests posting comments for other trends based on the comparison results. Specifically, the information processing device 1 compares the number of comments for each trend and suggests posting comments for trends where 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 where the number of comments is less than the predetermined number. This increases the diversity of comments, thereby improving the quality of comment-related services.

Next, the information processing device 1 determines whether the classification results obtained by the processing of step S5 satisfy predetermined conditions (step S7). Specifically, the information processing device 1 determines whether a predetermined number or more of comments with trends among multiple pre-set trends have been posted (classified).

Next, if the classification results satisfy a predetermined condition, specifically, if a predetermined number or more of comments with a predetermined trend among multiple preset trends have been posted, the information processing device 1 provides the posted comments for each trend together (step S8-1). In other words, rather than providing the posted comments immediately, comments with various trends are provided together at the timing when they are posted. This makes it possible to prevent only comments with a biased trend from being provided, and since comments with various trends can be provided together, the diversity of comments can be increased. In other words, the information processing device 1 according to the embodiment can improve the quality of services related to comments.

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 they posted (step S8-2). For example, the information processing device 1 provides a collection of comments with trends that exclude the trend of the comment posted by the user from 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.

Next, we will explain Process 2 shown in Figure 1B. Note that in Figure 1B, processes that overlap with those shown in Figure 1A may be omitted for the sake of convenience.

In process 2 shown in FIG. 1B, the information processing device 1 collects (acquires) rating information indicating ratings for each comment posted on content (step S31). The rating information includes whether the rating for the comment is favorable (agrees with the comment) or negative (does not agree with the comment).

Next, the information processing device 1 determines the diversity of ratings based on the acquired rating information (step S32). For example, if the ratings for the comment are all positive (or all negative), the information processing device 1 determines that there is no diversity of ratings. In other words, if the number of ratings (number of users) for both positive and negative ratings is balanced (if the difference in the number of ratings is less than a predetermined value), the information processing device 1 determines that there is diversity of ratings.

Furthermore, the information processing device 1 determines the diversity of the ratings included in each combination of multiple comments. Specifically, the information processing device 1 determines whether there is diversity in the ratings of the comments included in the set. For example, when user A rates comment A and comment B, if the user rates 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 diversity is determined based on user A's ratings in the above, diversity may also be determined based on the ratings of a group including multiple users with similar user information. Note that the number of comments included in a set may be three or more.

In addition, the information processing device 1 determines that there is diversity in evaluations for a group that includes comments with only positive evaluations and comments with only negative evaluations. In other words, when viewing the evaluations on a group-by-group basis, the information processing device 1 determines that there is diversity in evaluations if there is a balance between positive and negative evaluations.

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 that comments determined to have diverse evaluations are to be displayed. Furthermore, the information processing device 1 determines that comments in a group determined to have diverse evaluations are to be displayed.

The information processing device 1 then displays the comments (set of comments) determined to be displayed 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 ratings.

Next, we will explain process 3 shown in Figure 1C. Note that in Figure 1C, processes that overlap with those shown in Figures 1A and 1B may be omitted for the sake of convenience.

In process 3 shown in Figure 1C, the information processing device 1 collects each comment on the content (step S51).

Next, the information processing device 1 extracts comments whose content satisfies predetermined content conditions (step S52). For example, the information processing device 1 extracts comments whose content is inappropriate (scores above a predetermined value) using 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.

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 a favorable rating to the extracted comment.

Next, the information processing device 1 detects other comments whose content meets predetermined conditions based on the specified user's (specific user's) agreement with other comments (step S54). For example, the information processing device 1 detects other comments where a specified number of specific users have agreed with the other comment as other comments that meet the predetermined conditions. In other words, for other comments whose content was determined to be not inappropriate in step S52, the information processing device 1 detects other comments with a large number of specific users as comments whose content is inappropriate. This makes it possible to find other inappropriate comments based on the agreement with other comments of users who have agreed with the inappropriate comment, thereby improving the quality of comment-related services.

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).

Next, we will explain process 4 shown in Figure 1D. Note that in Figure 1D, processes that overlap with those shown in Figures 1A to 1C may be omitted for the sake of convenience.

In process 4 shown in FIG. 1D, the information processing device 1 distributes content to the user terminal 100 (step S71). Next, the information processing device 1 accepts comments on the content from the user (step S72).

Next, the information processing device 1 classifies each received comment by trend in comment content or by user information of the posting user (step S73).

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

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

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

This allows us to provide comments with different trends and user information than the comments viewed by the viewing user, thereby improving the quality of our comment-related services.

Next, an example configuration of an information processing system S according to an embodiment will be described using FIG. 2. FIG. 2 is a block diagram showing an example configuration of an 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, multiple user terminals 100, and multiple requesting terminals 200 are connected to a network N via wired or wireless connections. The network N is, for example, the Internet, a WAN (Wide Area Network), a LAN (Local Area Network), or the like.

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 processes 1 to 4 described above with reference to Figures 1A to 1D.

In addition, the information processing device 1 is an information processing device that works in conjunction 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), as well as various data, and is realized by a server device, a cloud system, etc.

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

The user terminal 100 is a terminal device owned by a user who views content, comments, and ratings on comments. The user terminal 100 can be any type of terminal device, such as a smartphone, desktop PC, notebook PC, or 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.

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, desktop PC, notebook PC, or tablet PC. The requesting terminal 200 transmits various information to the information processing device 1, etc., and receives information provided by the information processing device 1, etc.

Next, an example configuration of the information processing device 1 will be described with reference to Figure 3.

Figure 3 is a diagram showing an example configuration of an information processing device 1 according to an embodiment. As shown in Figure 3, the information processing device 1 has a communication unit 2, a control unit 3, and a memory 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 decision unit 36, a detection unit 37, a reception control unit 38, a suggestion unit 39, and a provision unit 40. The memory unit 4 stores user information 41, content information 42, and comment information 43.

The communication unit 2 is realized, for example, by a network interface card (NIC). The communication unit 2 is connected to the network via a wired or wireless connection.

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

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

User information 41 is information about a user. Figure 4 shows an example of user information 41. As shown in Figure 4, user information 41 includes items such as "User ID" and "User Information."

"User ID" is identification information that identifies a user. "User information" is information about a user. "User information" includes, for example, attribute information related to 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.).

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

"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 content, and includes text information, image information, etc. "Comment information" is information on comments posted in response to the content, and the "Comment ID" of comment information 43 described below is entered.

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

"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 given by users of the comment, and includes, for example, information about the number of users who gave favorable ratings and the number of users who gave negative ratings.

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).

The distribution unit 31 distributes content to the user terminal 100. The distribution unit 31 acquires the content to be distributed from the requesting user via the requesting terminal 200 and stores it 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.

In addition to the content, the distribution unit 31 also distributes comments posted by other users who viewed the content, as well as ratings posted by other users on the comments.

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

The identification unit 33 identifies trends in the comment content based on the comment content. Trends can be identified, for example, using a classification model in machine learning. Specifically, the identification unit 33 identifies comment trends by inputting the comments into a classification model that has learned the comment content and trends as a dataset.

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

The identification unit 33 also identifies users who agree with a comment whose content has been determined by the determination unit 35 (described below) to satisfy a predetermined content condition. For example, the identification unit 33 identifies users who have given a favorable evaluation to a comment that satisfies a predetermined content condition.

The identification unit 33 also identifies a tendency associated with a posting user based on the comment-related behavioral history of the posting user who posted the comment. The comment-related behavioral history may be, for example, a history of past comment postings and a history of evaluations of comments posted by other posting users.

For example, the identification unit 33 identifies a trend corresponding to the posting user based on the trends of comments posted in the past by the posting user. Specifically, the identification unit 33 tallyes 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, based on the tallying results, identify multiple trends with a predetermined number of comments or more as trends (multiple) corresponding to the posting user.

The identification unit 33 also identifies a tendency associated with the posting user based on the content of the evaluation history for comments posted by other posting users. Specifically, when a posting user gives a favorable evaluation to a comment posted by another posting user, the identification unit 33 identifies the tendency of the evaluated comments as a tendency associated with the posting user.

In addition, when a poster gives a negative rating to a comment posted by another poster, the identification unit 33 identifies a trend different from the trend of the rated comment as a trend corresponding to the poster.

In addition, a method for selecting trends different from the trend of the rated comment can be selected based on the distance between trends in the distributed representation space. In other words, the identification unit 33 identifies trends in the distributed representation space that are a predetermined distance or more away from the trend of the rated comment as trends corresponding to the posting user.

The classification unit 34 classifies each comment according to the identified trend or the identified user information. Specifically, the classification unit 34 sorts each comment according to the identified trend or the identified user information. More specifically, the classification unit 34 classifies the identified trend of the comment into one of multiple pre-set trends. Such classification is possible by inputting the identified trend and the trend to which the identified trend belongs from the multiple pre-set trends as a dataset into a trained model.

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 predetermined number has been reached. Furthermore, for trends or user information for which a predetermined number of comments or more 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 for 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.

The classification unit 34 may also re-define the multiple trends to be classified based on the classification results. Specifically, if the number of comments classified into a specific trend is significantly large, in other words, if 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.

Such a method of reconstructing trends can be achieved, for example, by creating a distributed representation of each comment based on its content, and automatically generating axes that separate areas in the distributed representation space as trends.

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 previously posted comments. Specifically, the determination unit 35 determines that the above condition is satisfied if the trend of the identified comment is similar to the trend into which a predetermined number of previously posted comments have been classified.

The determination unit 35 also calculates the ratio of the number of posted comments for each trend to the number of posted comments for all trends, and determines that the above condition is met if the trend for which this ratio is equal to or greater than a predetermined value is similar to the trend of the identified comment.

The determination unit 35 may also 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, based on the number of comments for each trend posted in the past by the posting user. The risk is set higher for trends with a larger number of comments. The determination unit 35 then calculates a score indicating the degree of similarity between the identified comment trend and the tendencies into which a predetermined number of posted comments have been classified, and determines whether the above conditions are met based on this score and the above risk.

The determination unit 35 also determines the diversity of 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 if the evaluations for the comment are all favorable (or all negative) evaluations. In other words, the determination unit 35 determines that there is diversity of evaluations if the number of favorable and negative evaluations (number of users) is balanced (if the difference in the number of evaluations is less than a predetermined value).

The determination unit 35 may also determine the diversity of ratings in multiple comments. For example, the determination unit 35 determines the diversity of ratings included in each set for each combination of multiple comments. Specifically, the determination unit 35 determines whether there is diversity in the ratings of the comments included in the set. For example, when user A evaluates comment A and comment B, and gives a favorable evaluation for comment A and a negative evaluation for comment B, the determination unit 35 determines that there is diversity in the ratings of the set of comment A and comment B. Note that, although diversity is determined based on user A's ratings in the above, diversity may also be determined based on the ratings of a group including multiple users with similar user information. Note that the number of comments included in a set may be three or more.

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

The determination unit 35 may also determine the diversity of users who provided ratings. For example, the determination unit 35 determines that there is diversity in ratings when the attribute information of the users who provided favorable (or negative) ratings for a comment is not similar, i.e., when users with various attributes provided favorable ratings.

On the other hand, if the attribute information of each user who gave a favorable (or negative) rating to a comment is similar, i.e., if users with similar attributes give favorable ratings, the determination unit 35 determines that there is no diversity in the ratings.

The determination unit 35 may also group users based on user information and determine the diversity of evaluations on a group-by-group basis. For example, the determination unit 35 may determine that there is diversity in evaluations when the information (e.g., group attribute information) of groups that have given favorable (or negative) evaluations to a comment is not similar, i.e., when groups with various attributes have given favorable evaluations.

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 determination unit 35 determines that there is no diversity in the evaluations.

The determination unit 35 also determines whether the comment content satisfies predetermined content conditions. 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 content conditions are satisfied if the score output from the model is equal to or greater than a predetermined value. In other words, the content conditions are conditions for determining whether the comment content is inappropriate (violent, obscene, defamatory, etc.). The feature information also includes the presence or absence of violent, obscene, or defamatory sentences contained in the comment content, as well as words related to these expressions, and the number of such sentences and words. The determination unit 35 may also correct the above model based on other comments that satisfy predetermined conditions (inappropriate) detected by the detection unit 37, which will be described later.

The determination unit 36 determines whether or not to display a comment based on the determination result of the determination unit 35. Specifically, the determination unit 36 determines whether or not to display a comment based on the determination result of the diversity of evaluations. Specifically, the determination unit 36 determines comments that are determined to have diversity of evaluations as comments to be displayed. Furthermore, the determination unit 36 determines comments in a group that are determined to have diversity of evaluations as comments to be displayed.

The determination unit 36 also determines the display order of comments based on the results of the evaluation diversity determination. For example, among multiple comments for which a large number of users have given favorable (or negative) evaluations, the determination unit 36 displays comments determined to have evaluation diversity at the top.

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 agreed with the comment satisfying the content condition) with other comments. Specifically, the detection unit 37 detects, as other comments satisfying the predetermined condition, other comments for which a predetermined number of users, among the identified users, agree with the other comment. Alternatively, the detection unit 37 may detect, as other comments satisfying the predetermined condition, other comments for which the ratio of the number of users identified by the identification unit 33 to the total number of users who agree with the other comment is equal to or greater than a predetermined value. In other words, the detection unit 37 detects, as comments whose content is inappropriate, other comments that a majority of the identified users agree with, among other comments determined by the determination unit 35 to be not inappropriate. Note that the other comments may be comments posted for the same content (e.g., the same news article) as the comment satisfying the predetermined content condition, or may be comments posted for 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 identified user's agreement 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.

The detection unit 37 may also detect other comments that satisfy predetermined conditions by taking into account the identified user's context information. For example, if the user's context information when they agreed with an inappropriate comment is similar to the user's context information when they agreed 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 they feel like agreeing with the inappropriate comment (a contrarian mood), and detects other comments that they agreed with when they felt like agreeing 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. 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.

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

The reception control unit 38 may also suggest changing the trend of the entered comments as a control to prevent the posting of comments from being accepted. In such a 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.

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 results. Suggestions for posting a comment may be made, for example, when viewing content or when entering a comment.

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 if the difference in the ratio between the trends is equal to or greater than a predetermined value.

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

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, if 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, if 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.

The providing unit 40 provides various information to the user. For example, the providing unit 40 provides comments for each trend when the classification results by the classifying unit 34 satisfy a predetermined condition. Specifically, the providing unit 40 determines that a predetermined condition is satisfied when a predetermined number or more of the multiple pre-defined trends into which comments have been classified are satisfied, and provides the posted comments for each trend in a group. After providing the comments in a group, the providing unit 40 resets the classification results, re-classifies newly posted comments, and provides the newly posted comments for each trend in a group when the above-mentioned predetermined condition is satisfied. In other words, the providing unit 40 provides the comments for each trend in a group each time the classification results satisfy the predetermined condition.

The comments to be provided may be all comments for each trend, or for trends with 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 prioritizing comments from users who have posted a large number of comments in the past, or may simply select comments at random.

The providing unit 40 may also select a representative comment based on the similarity of the 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 degree of similarity to the attribute information of the viewing user as the representative comment.

The providing unit 40 also provides a viewing user who has viewed a comment with one of the tendencies classified by the classification unit 34 with a comment with a different tendency from that tendency. The providing unit 40 also provides a viewing user who has viewed a comment corresponding to one of the user information classified by the classification unit 34 with a comment corresponding to user information different from that user information.

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 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 using the above method when the number of comments of the trend to be provided is a predetermined number or more.

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

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 comments that tend to correspond to the posting user.

The providing unit 40 also displays comments that tend to correspond to the posting user in different display formats from comments that tend to differ from the corresponding trend. Examples of display formats include background color, text color, text underlining, text font, and text size.

The providing unit 40 also provides information about other comments that satisfy the predetermined conditions (inappropriate comments) detected by the detecting unit 37 to, for example, an administrator who manages the content (or comments).

Next, the processing procedures for processes 1 to 4 executed by the information processing device 1 according to the embodiment will be described using Figures 7 to 10. Figures 7 to 10 are flowcharts showing the processing procedures for processes 1 to 4 executed by the information processing device 1 according to the embodiment.

First, we will explain Process 1 using Figure 7. As shown in Figure 7, the control unit 3 first distributes content to the user terminal 100 (step S101).

Next, the control unit 3 accepts user comments on the distributed content (step S102).

Next, the control unit 3 identifies trends in the comment content (step S103).

Next, the control unit 3 classifies the comments according to the identified trends (step S104).

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

If the identified trend is not similar to the trend of comments posted a predetermined number or more (step S105: No), the control unit 3 determines whether the classification results satisfy predetermined conditions (step S106).

If the classification results satisfy the predetermined conditions (step S106: Yes), the control unit 3 compiles comments for each trend and provides them to the user (step S107), and then ends the process.

On the other hand, if the classification results do not satisfy the predetermined conditions (step S106: No), the control unit 3 terminates the process without providing a comment.

Furthermore, 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 then ends the processing.

Next, we will explain process 2 using Figure 8. As shown in Figure 8, the control unit 3 collects evaluation information indicating evaluations of comments (step S201).

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

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

The control unit 3 then provides the determined comment to the user (step S204) and ends the process.

Next, we will explain process 3 using Figure 9. As shown in Figure 9, the control unit 3 collects each user's comment on the content (step S301).

Next, the control unit 3 extracts comments whose content meets predetermined content conditions (step S302).

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

Next, the control unit 3 detects other comments based on the identified user's agreement with other comments (step S304) and terminates the process.

Next, we will explain Process 4 using Figure 10. As shown in Figure 10, the control unit 3 classifies each comment by the tendency of the comment content (or by user information) (step S401).

Next, the control unit 3 identifies the trends (user information) of the comments viewed by the viewing user (step S402).

Next, the control unit 3 extracts comments with trends (or user information) different from the identified trends (or user information) (step S403).

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

〔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.

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

For example, part or all of the memory unit 4 shown in FIG. 3 may be stored in a storage server or the like, rather than being stored in each device. In this case, each device obtains various information by accessing the storage server.

[Hardware configuration]
The information processing device 1 according to the embodiment described above is realized by, for example, a computer 1000 configured as shown in Fig. 11. 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 a calculation device 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.

The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and secondary storage device 1050, and programs read from the input device 1020, and executes various processes. The primary storage device 1040 is a memory device, such as 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), HDD (Hard Disk Drive), flash memory, etc.

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

The input device 1020 may be a device that reads information from optical recording media such as a CD (Compact Disc), DVD (Digital Versatile Disc), or 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.

Network IF 1080 receives data from other devices via network N and sends it to the computing device 1030, and also transmits data generated by the computing device 1030 to other devices via network N.

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.

For example, when the computer 1000 functions as the information processing device 1, the arithmetic unit 1030 of the computer 1000 realizes the functions of the control unit 3 by executing a program loaded onto the primary storage device 1040.

〔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.

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 the tendency of the content of comments entered by users that are posted on specified 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 prevent the posting of comments based on the determination result.

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 for 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.

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 piece of 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.

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.

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 piece of 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.

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.

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 specific 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.

The information processing device 1 according to each of the above-described embodiments can improve the quality of services related to comments.

Although several 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, including those described in the Disclosure of the Invention section, with various modifications and improvements based on the knowledge of those skilled in the art.

〔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.

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

Furthermore, the processes described in the above embodiments can be combined as appropriate to the extent that the processing content is not contradictory.

Furthermore, the above-mentioned "section, module, unit" can be interpreted as "means" or "circuit." For example, the control unit 3 can be interpreted as control means or control circuit.

Reference Signs List 1 Information processing device 2 Communication unit 3 Control unit 4 Storage unit 31 Distribution unit 32 Acquisition unit 33 Identification unit 34 Classification unit 35 Determination unit 36 Determination unit 37 Detection unit 38 Acceptance control unit 39 Proposal unit 40 Provision unit 41 User information 42 Content information 43 Comment information 100 User terminal 200 Requester terminal S Information processing system

Claims (7)

an identification unit that inputs each comment posted to a predetermined content into a learning model that has learned comment content and comment content trends as a data set, and identifies the trend that is an output result from the learning model;
a classification unit that classifies the comments according to the tendencies of the comment contents ;
a storage unit that assigns a comment ID to each of the comments and stores comment information linked to the tendency, which is a classification result;
a providing unit that provides , based on the comment information, a comment having a different tendency to a user who has previously provided the comment having a predetermined tendency ;
An information processing device comprising: The classification unit
Each comment is classified into one of several predefined trends,
The providing unit
The information processing device according to claim 1 , further comprising: providing, from among the plurality of tendencies, the comments of a tendency different from the tendency of the comments provided to the user . The classification unit
The information processing device according to claim 1 , wherein each comment is converted into a distributed representation based on the comment content, and an axis that divides an area of the distributed representation space is set as the tendency. The providing unit
The information processing device according to claim 1 , wherein each tendency is represented as a distributed representation, and a comment having a tendency that is most distant from the tendency of the comment provided to the user in a distributed representation space is provided. The providing unit
The information processing device according to claim 1 , wherein each tendency is represented as a distributed representation, and comments of a plurality of tendencies that are at least a predetermined distance away from the tendency of the comment provided to the user are provided in a distributed representation space. 1. A computer-implemented information processing method, comprising:
an identifying step of inputting each comment posted to a predetermined content into a learning model that has been trained using comment content and comment content trends as a data set, and identifying the trends that are output results from the learning model;
a classification step of classifying the comments according to the tendencies of the content of the comments ;
a storage step of assigning a comment ID to each of the comments and storing comment information linked to the tendency, which is a classification result, in a storage unit;
a providing step of providing , based on the comment information, a comment having a different tendency to a user who has previously provided the comment having a predetermined tendency ;
An information processing method including: an identification step of inputting each comment posted to a predetermined content into a learning model that has been trained using comment content and comment content trends as a data set, and identifying the trends that are output results from the learning model;
a classification step of classifying the comments according to the tendencies of the content of the comments ;
a storage step of assigning a comment ID to each of the comments and storing comment information linked to the tendency, which is the classification result, in a storage unit;
a provision step of providing , based on the comment information, a user who has provided the comment according to a predetermined trend in the past with a comment according to a different trend from the previous trend ;
An information processing program that causes a computer to execute the above.