JP5952756B2 - Prediction server, program and method for predicting future number of comments in prediction target content - Google Patents

Description

  The present invention relates to a technique for analyzing a tendency of interest of an unspecified number of users for content to be predicted.

  In recent years, various contents have been released to an unspecified number of third parties via the Internet. The public content is, for example, various media contents such as news articles, Web pages, music contents, electronic books, and TV broadcast contents, and is accessible by an unspecified number of third parties.

  On the other hand, comment texts from an unspecified number of third parties are sent to sites such as blogs (Web log) and mini blogs (for example, twitter (registered trademark)) via the Internet. It is actively transmitted. Such comment sentences are often discussed on a common topic. As such a common topic, there is public content distributed to an unspecified number of third parties as described above.

  Conventionally, there is a technique of searching for comment text related to such public content and publishing the content ranking format according to the number of comments (see, for example, Non-Patent Document 1). According to this technology, keywords are extracted from a large number of posted comments, and the topics and trends that are excited by twitter are analyzed in real time, and the ranking is clearly indicated to the user.

  In addition, as a specific service technique, there is a technique for predicting the box office income of a movie based on, for example, the number of comments and contents on the twitter related to the movie (see, for example, Non-Patent Document 2). Specifically, information used for prediction includes the number of tweets and the positive negative ratio (positive contents / negative contents of the contents of tweets) and the number of tweets and positive negative ratios after the release of the movie. A movie with a large number of tweets and a large number of positive comments is expected to have a higher box office income. On the other hand, it is predicted that the performance income will be lower for movies with fewer tweets and more negative comments.

NEC Big Robe, “Tappur Trend”, [online], [Search August 27, 2012], Internet <URL: http://tr.twipple.jp/> Sitaram Asur and Bernardo A. Huberman, HP Labs, “Predicting the Future With Social Media”, Proc. Of the 2010 IEEE / WIC / ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '10), vol. 1, pp. 492-499 A. J. Smola and B. Scholkopf, "A tutorial on support vectorregression", Journal of Statistics and Computing, vol. 14, No. 3, pp.199-222, 2004. "Linear regression (single regression)", [online], [February 10, 2013 search], Internet <URL: http://aoki2.si.gunma-u.ac.jp/lecture/Regression/sreg/ sreg.html>

  According to the technique described in Non-Patent Document 1, it is possible to specify content that an unspecified number of users are interested in at the current time. However, it will not be possible to identify content that will be of interest to an unspecified number of users in the future. Also, since a sufficient number of comments cannot be obtained immediately after news distribution, it is difficult to accurately determine the ranking.

  According to the technique described in Non-Patent Document 2, since the target content is relatively limited like a movie, it is compared with how much an unspecified number of users will be interested in the future. Can be predicted with high accuracy. However, even when this technology is used, when the content to be transmitted and the content of which is diverse, such as a news article, is targeted for prediction, it is difficult to obtain sufficient prediction accuracy. The reason is based on the fact that there is a difference in the tendency of the content of the comment with respect to various contents of the content. In particular, comments on current news articles increase in quantity, and it is difficult to detect the tendency of the future number of comments on specific prediction target content. Also, according to this technique, comments on a movie are relatively long and require observation for about one week or longer. Since comments on news are generally posted within 24 hours after distribution, it is originally desirable to be able to predict in a short period of about 1 hour.

  In contrast to such conventional technology, the inventors of the present application observe a time-series change in the number of comments for a current event such as news. I thought that.

  FIG. 1 is an explanatory diagram showing prediction accuracy in a single model.

According to FIG. 1, the graph of the posting tendency of the number of postings according to the passage of time is represented. For example, it is assumed that there are the following two posting tendencies.
“Cliff”: Increases immediately after distribution, then decreases rapidly “Mountain”: Increases slowly after distribution, then decreases.
When using different prediction target contents with different posting tendencies, using a single prediction model, even if the average prediction accuracy can be slightly improved, the prediction accuracy of individual prediction target contents is greatly increased. It is not possible.

  Therefore, the present invention analyzes the future trend of interest among an unspecified number of users with high accuracy from the number of short-term comments in the future, even for prediction target content such as general news articles. It is an object of the present invention to provide a prediction server, a program, and a method that can be used.

According to the present invention, it is possible to communicate with a site server that sends text comments among a plurality of contributors, and a prediction server that predicts the future number of comments in the prediction target content,
For each content, learning the transition state (temporal change) of the number of comments in each unit time according to the passage of time by identifying which of the multiple posting trends is based on the number of comments at the initial action determination time Learning information storage means stored in advance as information;
Prediction target comment search means for counting the number of comments in each unit time in the initial movement determination time for the prediction target content acquired from the site server;
Initial movement tendency identifying means for identifying which of the posting tendencies from the transition state of the number of comments of the initial movement determination time in the prediction target content;
Initial motion determination time search means for searching for content similar to the transition state of the number of comments of the initial motion determination time in the prediction target content from the same transition type in the learning information storage means;
Comment number predicting means for deriving the transition state of the number of comments corresponding to the searched content after the initial motion determination time as the transition state of the future number of comments in the prediction target content.

  According to another embodiment of the prediction server of the present invention, the posting tendency is either “cliff shape” that decreases thereafter or “mountain shape” that increases thereafter with respect to the number of comments in the initial motion determination time. It is also preferable.

According to another embodiment of the prediction server of the present invention,
The learning information storage means includes a cliff-shaped regression model calculated based on the transition state of the number of comments for the cliff-shaped content, and a mountain-shaped regression calculated based on the transition state of the number of comments for the mountain-shaped content. Accumulate models and
It is also preferable that the initial movement determination time search means use the same transition type regression model in the learning information storage means.

  According to another embodiment of the prediction server of the present invention, the regression model is preferably based on Support Vector Regression (SVR).

According to another embodiment of the prediction server of the present invention,
An overall trend identifying means for identifying whether the number of comments in the initial motion determination time for the content is a “cliff” that subsequently decreases or a “mountain” that increases;
The initial movement tendency identification means is a supervised learning function that inputs, as learning data, the “cliff shape” and “mountain shape” output from the overall tendency identification means together with the transition state of the number of comments at the initial movement determination time of the content. Is also preferable.

  According to another embodiment of the prediction server of the present invention, the initial movement tendency identifying means is preferably a support vector machine or C4.5.

  According to another embodiment of the prediction server of the present invention, it is also preferable that the initial movement tendency identifying means identifies, as a determination element, the slope of the approximate curve in the transition state of the number of comments for the initial movement determination time in the prediction target content.

  According to another embodiment of the prediction server of the present invention, it is also preferable that the initial movement tendency identifying unit identifies the distribution time of the prediction target content as a determination element.

  According to another embodiment of the prediction server of the present invention, it is also preferable that the initial movement tendency identifying means identifies the content type of the prediction target content as a determination element.

According to another embodiment of the prediction server of the present invention,
The site server is a blog site server,
The prediction server
A prediction target keyword extracting unit that extracts a keyword group included in the prediction target content;
It is also preferable that the prediction target comment search unit searches a plurality of comments from the blog site server using the keyword group extracted by the prediction target keyword extraction unit as a key, and counts the number of comments in each unit time according to the passage of time. .

According to the present invention, a prediction program that can communicate with a site server that sends text comments among a plurality of contributors and that functions as a computer mounted on a server that predicts the future number of comments in the prediction target content Because
For each content, learning the transition state (temporal change) of the number of comments in each unit time according to the passage of time by identifying which of the multiple posting trends is based on the number of comments at the initial action determination time Learning information storage means stored in advance as information;
Prediction target comment search means for counting the number of comments in each unit time in the initial movement determination time for the prediction target content acquired from the site server;
Initial movement tendency identifying means for identifying which of the posting tendencies from the transition state of the number of comments of the initial movement determination time in the prediction target content;
Initial motion determination time search means for searching for content similar to the transition state of the number of comments of the initial motion determination time in the prediction target content from the same transition type in the learning information storage means;
The computer is made to function as comment number prediction means for deriving the transition state of the number of comments after the initial motion determination time corresponding to the searched content as the transition state of the future number of comments in the prediction target content.

According to the present invention, it is possible to communicate with a site server that sends text comments between a plurality of contributors, and a prediction server comment number prediction method for predicting the future number of comments in a prediction target content,
For each content , the prediction server identifies the transition state (temporal change) of the number of comments for each unit time according to the passage of time, among the multiple posting tendencies, based on the number of comments at the initial action determination time And having a learning information storage unit stored in advance as learning information,
The prediction server
A first step of counting the number of comments in each unit time in the initial motion determination time for the prediction target content acquired from the site server;
A second step of identifying a posting tendency from the transition state of the number of comments in the initial motion determination time in the prediction target content;
A third step of searching for content similar to the transition state of the number of comments in the initial motion determination time in the prediction target content from the same transition type in the learning information storage unit ;
It retrieved the comments transition state after that initial determination time corresponding to the content, and executes a fourth step of deriving a transition state of the future number of comments in the prediction target content.

  According to the prediction server, program, and method of the present invention, even if the content is a prediction target such as a general news article, the future interest of an unspecified number of users can be calculated from the number of comments in the short time in the future. Trends can be analyzed with high accuracy.

It is explanatory drawing showing the prediction accuracy in a single model. It is a system configuration diagram in the present invention. It is a functional block diagram of the prediction server in this invention. It is explanatory drawing showing the process of a learning information storage part, an initial movement tendency identification part, an initial movement determination time search part, and an overall tendency identification part. It is a graph showing the ratio of a cliff shape or a mountain shape according to delivery time. It is a graph showing the ratio of the mountain shape for every content classification. It is explanatory drawing showing the process in the ranking publicizing part of a prediction server.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 2 is a system configuration diagram according to the present invention.

  According to FIG. 2, the content publishing server 3 is connected to the Internet. The content publishing server 3 distributes public content to an unspecified number of third parties. The public content is various media content, and may be, for example, a news article, a web page, music content, an electronic book, or a television broadcast content.

  In addition, according to FIG. 2, the blog site server 2 is further connected to the Internet, and text comments can be transmitted among a plurality of contributors. The blog site server 2 may be a twitter (registered trademark) site, for example.

  In the following description, it is assumed that the blog site server 2 and the content publishing server 3 are separately installed on the Internet (see FIG. 2). However, a site server in which these functions are integrated may be used. In the case of such a site server, a comment is associated with each content and released. For example, there is a site server such as YouTube (registered trademark).

  The terminal 4 is a personal computer, a portable terminal, a smartphone, a television, or the like, and can access the content publishing server 3 and the blog site server 2. An unspecified number of users can post comment text to the blog site server 2 while browsing content published by the content publishing server 3 using the terminal 4, and can also browse other people's comment text.

  According to the present invention, the prediction server 1 is further connected to the Internet, and can communicate with the content publishing server 3 and the blog site server 2. The prediction server 1 in the present invention can predict the future number of comments in the prediction target content. Thereby, the user can know the content that will be noticed in the future by accessing the prediction server 1 from the terminal 4.

  FIG. 3 is a functional configuration diagram of the prediction server in the present invention.

  According to FIG. 3, the prediction server 1 includes a learning information storage unit 10, a prediction target keyword extraction unit 11, a prediction target comment search unit 12, an initial movement tendency identification unit 13, an initial movement determination time search unit 14, and the whole A trend identification unit 15, a comment number prediction unit 16, and a ranking disclosure unit 17 are included. These functional components are realized by executing a program that causes a computer mounted on the server to function. Further, the processing order of each functional component can be understood as a comment number prediction method.

[Prediction target keyword extraction unit 11]
The prediction target keyword extraction unit 11 inputs a prediction target content as a key, and extracts a keyword group included in the prediction target content. The prediction target content may be a Web page or only a URL (Uniform Resource Locator). When the URL is only the URL, the prediction target keyword extraction unit 11 acquires a Web page based on the URL from the content publishing server 3. It is assumed that these contents include at least text.

  Next, the prediction target keyword extraction unit 11 extracts words from the text included in the content by morphological analysis. “Morphological analysis” refers to a technique in which sentences are divided into meaningful words and the part of speech and contents are discriminated using a dictionary. The “morpheme” means the smallest unit having meaning among the elements of the sentence.

  Next, the prediction target keyword extraction unit 11 extracts a characteristic word as a keyword based on TF-IDF (Term Frequency-Inverse Document Frequency). TF-IDF is a technology that weights each word, expresses sentences from a query in a vector space, and ranks them based on the similarity between the sentences and the query. The higher the ranked value, the more important keywords are recognized.

For example, it is assumed that the following news article exists as the prediction target content.
“[Japan wins for the first time in 42 years = Blocking the 8th consecutive victory in the US-Softball Women] The last day of the softball women ’s world championship will be held in Whitehorse, Canada on the 22nd. Break 10 times, 2-1 and <delivery at 11:30 on Monday, July 23>
For example, the following keyword group is extracted from this news article.
"Winner""Consecutivevictory""Softball"
Of course, the URL of the prediction target content itself may be extracted as a keyword.

[Prediction target comment search unit 12]
The prediction target comment search unit 12 searches the blog site server 2 for a plurality of comments using the extracted keyword group as a key. For example, the following comments are searched using the above keyword group as a key.
> Contributor ID: xxxyyy
> Attributes: 30s, male, office worker, sports> Number of followers: 200> Content: Finally! Japan wins for the first time in 42 years
http://www.news.jp/xxx
> Posting time: Monday, July 23, 12:30

  The prediction target comment search unit 12 searches a large number of comments related to the prediction target content. And about the said prediction object content, the number of comments of each unit time according to progress of time is counted. The transition state (graphed history information) of the number of comments according to the elapsed time is output to the determination time search unit 14.

  When the prediction server can communicate with a site server in which the function of the blog site server and the function of the content publishing server are integrated, the prediction target keyword extraction unit 11 is not an essential component. Further, the prediction target comment search unit 12 does not need to search a plurality of comments from the blog site server 2 using the extracted keyword group as a key. This is because the integrated site server has already transmitted a comment group in association with each content.

  FIG. 4 is an explanatory diagram illustrating processing of the learning information storage unit, the initial movement tendency identification unit, the initial movement determination time search unit, and the overall trend identification unit.

[Learning information storage unit 10]
For each content, the learning information storage unit 10 indicates the transition state (temporal change) of the number of comments in each unit time according to the passage of time, among any of a plurality of posting tendencies, depending on the number of comments at the initial action determination time. Is stored in advance as learning information. For each content, for example, for 24 hours after distribution, a posting tendency indicating a transition state of the number of comments every 10 minutes (unit time, slot) (time change of 144 slots) is stored. In other words, the posting tendency refers to a change in the number of postings with time.

As the posting tendency, for example, there is a “cliff shape” that decreases afterwards, or a “mountain shape” that increases afterwards with respect to the number of comments at the initial motion determination time. Here, the learning information storage unit 10 preferably stores the following two prediction models.
Cliff-type regression model: Calculated from the transition state of the number of comments for the cliff-shaped content Mountain-type regression model: Calculated from the transition state of the number of comments for the mountain-shaped content The regression model is a support vector regression (SVR (Support Vector) Regression)) (see, for example, Non-Patent Document 3). The regression model described in Non-Patent Document 3 is used for the purpose of predicting data after a specified time from time series data.

  A regression model is a quantitative analysis of how dependent variables can be explained by explanatory variables for dependent variables (objective variables such as cliff-type or mountain-type) and independent variables of continuous scale (explanatory variables, content posting tendency). Analyzed.

[Overall trend identification unit 15]
The overall tendency identifying unit 15 identifies whether the number of comments in the initial motion determination time for the content is “cliff type” that decreases thereafter or “mountain type” that increases. The overall tendency identifying unit 15 generates learning information to be stored in the learning information storage unit 10 by communicating with the blog site server 2 and the content publishing server 3.

The overall trend identification unit 15 identifies the slope of the approximate curve in the transition state of the number of comments in the collected content as a determination element. The increase / decrease tendency of the number of comments can also be calculated by the following formula.
The coordinates of each point (time, number of comments) = (x _i , y _i )
Average of time x: mu
Average number of comments y: nu
Σ _i (x _i −mu) (y _i −nu) / Σ _i (x _i −mu) ²
For example, it is also preferable to use linear regression (simple regression) for obtaining the slope of the approximate curve (see Non-Patent Document 4, for example).

In addition, as another method for identifying the cliff shape or the mountain shape, regarding the number of postings of content collected for learning, the number of initial motions T _N for the initial motion determination time (for example, the first hour) and the cumulative posting of 24 hours It may be derived from the ratio R between the number T _a.
R _{= T} A / T _N

  Then, the overall tendency identification unit 15 outputs the posting tendency (cliff shape or mountain shape) for each content to the learning information storage unit 10. When the initial movement tendency identifying unit 13 has a supervised learning function to be described later, the overall tendency identifying unit 15 also outputs the posting tendency for each content to the initial movement tendency identifying unit 13.

[Initial motion identification unit 13]
The initial movement tendency identifying unit 13 identifies a posting tendency from the transition state of the number of comments in the initial movement determination time in the prediction target content. For example, suppose that it is desired to predict the future number of comments for the prediction target content from the current time. At this time, the transition state in a fixed time (for example, 1 hour) that goes back from the current time is set as the initial motion determination time. As the simplest method for determining the initial movement tendency, the prediction data of the initial movement determination time (for example, 1 hour: N = 6 slots) is identified as follows.
“Cliff”: The number of comments tends to decrease between N slots “Mountain”: The number of comments tends to decrease between N slots

  That is, the initial movement tendency identifying unit 13 identifies the inclination of the approximate curve in the transition state of the number of comments at the initial movement determination time in the prediction target content as a determination element. The increase / decrease tendency of the number of comments in the N slots can also be calculated by the same formula as that of the overall trend identification unit 15 described above.

Further, as another method for identifying a cliff type or mountain type, like the whole tendency identification section 15 described above, the cumulative number of posts P _A prediction target content, the initial posts P _N of initial determination time, the overall trend From the ratio R calculated by the identification unit 13, it can also be calculated as follows.
P _A = R × P _N

Moreover, it is also preferable to add and use the following determination factors.
(1) The initial movement tendency identifying unit 13 identifies the distribution time of the prediction target content as a determination element. FIG. 5 is a graph showing a cliff-shaped or mountain-shaped ratio according to the distribution time. According to FIG. 5, the mountain type tends to have a high content ratio delivered early in the morning or in the morning. On the other hand, the cliff type tends to have a high content ratio in the evening and at night. This graph is statistically measured and recorded in advance.

(2) The initial movement tendency identifying unit 13 identifies the content type (genre) of the prediction target content as a determination element. FIG. 6 is a graph showing the ratio of the chevron for each content type. According to FIG. 6, for example, prediction target contents related to “Korea”, “China”, and “industry” tend to have a high ratio of Yamagata. On the other hand, for example, prediction target content related to “movie”, “music”, and “politics” tends to have a high ratio of being a cliff. This graph is also statistically measured and recorded in advance.

  In addition, it is also preferable to accumulate | store the user name of the content as learning data, and the ratio of a cliff shape or a mountain shape. A user name that appears characteristically in a cliff shape or a mountain shape can be used for identification. For example, the ratio of either a cliff shape or a mountain shape may exceed a threshold value, or an information amount criterion such as AIC (Akaike's Information Criterion) may be used.

  Further, as shown in FIG. 4, the initial movement tendency identification unit 13 includes, as learning data, “cliff shape” and “mountain shape” output from the overall trend identification unit 15 together with the transition state of the number of comments in the initial movement determination time of the content. May be a supervised learning function. Specifically, the supervised learning function may be a support vector machine or C4.5.

For example, the following is an example of the feature amount input to C4.5. The posting tendency of content collected for learning is input from the overall tendency identifying unit 15.

[Initial motion determination time search unit 14]
The initial motion determination time search unit 14 searches for content similar to the transition state of the number of comments in the initial motion determination time in the prediction target content from the same transition types in the learning information storage unit 10. It is preferable that the initial motion determination time search unit 14 uses the same transition type regression model in the learning information storage unit 10.

  For example, with respect to the prediction target content determined as the cliff shape by the initial movement tendency identifying unit 13, the content similar to the transition state is searched from the cliff shape in the learning information storage unit 10. Alternatively, a cliff-shaped regression model is used. On the other hand, for the prediction target content determined to have a mountain shape by the initial movement tendency identifying unit 13, the content similar to the transition state is searched from the mountain shape in the learning information storage unit 10. Alternatively, a mountain-shaped regression model is used.

[Comment number prediction unit 16]
The comment number prediction unit 16 derives the transition state of the number of comments after the initial motion determination time corresponding to the searched content as the transition state of the future number of comments in the prediction target content. In this way, by comparing the trend of the number of comments from the past to the present of the content to be predicted (transition state) with the transition state of many other contents collected in the past, the trend of the increase or decrease in the number of comments in the future Can be predicted.

[Ranking Disclosure Department 17]
The ranking publication unit 17 publishes, as page information, ranking information that is sorted in order from content with a large number of future comments derived by the comment number prediction unit 16 for a plurality of prediction target contents. In this case, the plurality of contents to be predicted may be selected by the user, or may be previously selected by the operator of the prediction server. For example, although the user is not attracting much attention at present, the user can know the content in which the number of comments increases rapidly thereafter.

  FIG. 7 is an explanatory diagram illustrating processing in the ranking disclosure unit of the prediction server.

  According to FIG. 7, the content is represented in the ranking format from the first place to the fifth place where the number of comments will increase in the future as viewed from the current time. For example, the number one content has a large number of comments even at the current time, but is expected to increase further in the future. For example, for the 4th content, the number of comments is not so large at the current time, but is expected to increase rapidly in the future. In this way, the user can know the content whose number of comments will increase in the future by browsing the ranking-type page. That is, the user can know in advance the content that is not yet a big topic on the net.

  As described above in detail, according to the prediction server, program, and method of the present invention, it is unspecified from the number of short-term comments in the future, even for a content to be predicted such as a general news article. It is possible to analyze the tendency of future interest in a large number of users with high accuracy.

In particular, according to the present invention, it is possible to predict the subsequent posting tendency in a relatively short time after the distribution of the prediction target content. Therefore, it is possible to provide a highly advanced service that prefetches news that has not yet become a big topic on the Internet and presents it to the user. Moreover, in order to identify the posting tendency, the prediction accuracy is increased as compared with the case of using a single model that does not identify the posting tendency. The inventors of the present application have obtained the following results of improvement in prediction accuracy.
In the case of a single model that does not identify the posting tendency: Prediction accuracy of about 55% In the case of multiple models that identify the posting tendency (cliff type or mountain type): Prediction accuracy of about 81%

  Various changes, modifications, and omissions of the above-described various embodiments of the present invention can be easily made by those skilled in the art. The above description is merely an example, and is not intended to be restrictive. The invention is limited only as defined in the following claims and the equivalents thereto.

DESCRIPTION OF SYMBOLS 1 Prediction server 10 Learning information memory | storage part 11 Prediction object keyword extraction part 12 Prediction object comment search part 13 Initial movement tendency identification part 14 Initial movement determination time search part 15 Overall tendency identification part 16 Comment number prediction part 17 Ranking publication part 2 Blog site server 3 Content publishing server 4 terminal

Claims (12)

A prediction server that can communicate with a site server that sends text comments between multiple contributors and predicts the future number of comments in the prediction target content,
For each content, learning the transition state (temporal change) of the number of comments in each unit time according to the passage of time by identifying which of the multiple posting trends is based on the number of comments at the initial action determination time Learning information storage means stored in advance as information;
Prediction target comment search means for counting the number of comments for each unit time in the initial motion determination time for the prediction target content acquired from the site server;
From the transition state of the number of comments of the initial motion determination time in the prediction target content, initial motion tendency identifying means for identifying which of the posting trends;
Initial motion determination time search means for searching for content similar to the transition state of the number of comments of the initial motion determination time in the prediction target content from the same transition type in the learning information storage means;
A prediction server comprising comment number prediction means for deriving a transition state of the number of comments corresponding to the searched content after the initial motion determination time as a transition state of a future number of comments in the prediction target content.   2. The posting tendency according to claim 1, wherein the posting tendency is either “cliff shape” that decreases thereafter or “mountain shape” that increases thereafter with respect to the number of comments in the initial motion determination time. Prediction server. The learning information storage means includes a cliff-type regression model calculated based on the transition state of the number of comments for the cliff-shaped content, and a Yamagata type calculated based on the transition state of the number of comments for the mountain-shaped content. Accumulate regression model,
The prediction server according to claim 2, wherein the initial motion determination time search means uses the same transition type regression model in the learning information storage means.   The prediction server according to claim 3, wherein the regression model is based on Support Vector Regression (SVR). From the transition state of the number of comments of the initial motion determination time for the content, further comprising an overall trend identifying means for identifying whether it is a “cliff shape” that subsequently decreases or a “mountain shape” that increases.
The supervised learning function for inputting the “cliff” and “mountain” output from the overall tendency identifying means together with the transition state of the number of comments at the initial motion determination time of the content as learning data The prediction server according to any one of claims 2 to 4, wherein the prediction server is.   The prediction server according to claim 5, wherein the initial movement tendency identification means is a support vector machine or C4.5.   The said initial movement tendency identification means identifies the inclination of the approximated curve in the transition state of the number of comments of the initial movement determination time in the said prediction object content as a determination element, Either of Claim 5 or 6 characterized by the above-mentioned. Prediction server.   The prediction server according to any one of claims 5 to 7, wherein the initial movement tendency identification means identifies a distribution time of the prediction target content as a determination element.   The prediction server according to any one of claims 5 to 8, wherein the initial movement tendency identification unit identifies a content type of the prediction target content as a determination element. The site server is a blog site server,
The prediction server
A prediction target keyword extraction unit that extracts a keyword group included in the prediction target content;
The prediction target comment search means searches for a plurality of comments from the blog site server using the keyword group extracted by the prediction target keyword extraction means as a key, and counts the number of comments in each unit time according to the passage of time. The prediction server according to any one of claims 1 to 9, wherein: A prediction program that allows a computer mounted on a server that can communicate with a site server that sends text comments between a plurality of contributors and predicts the future number of comments in the prediction target content,
For each content, learning the transition state (temporal change) of the number of comments in each unit time according to the passage of time by identifying which of the multiple posting trends is based on the number of comments at the initial action determination time Learning information storage means stored in advance as information;
Prediction target comment search means for counting the number of comments for each unit time in the initial motion determination time for the prediction target content acquired from the site server;
From the transition state of the number of comments of the initial motion determination time in the prediction target content, initial motion tendency identifying means for identifying which of the posting trends;
Initial motion determination time search means for searching for content similar to the transition state of the number of comments of the initial motion determination time in the prediction target content from the same transition type in the learning information storage means;
A server that causes a computer to function as comment number prediction means for deriving the transition state of the number of comments after the initial motion determination time corresponding to the searched content as the transition state of the future number of comments in the prediction target content Prediction program. A method of predicting the number of comments of a prediction server that can communicate with a site server that sends text comments between a plurality of contributors and predicts the number of future comments in the prediction target content,
For each content , the prediction server determines whether the transition state (temporal change) of the number of comments in each unit time according to the passage of time is one of a plurality of posting tendencies according to the number of comments in the initial action determination time. Identify and have a learning information storage unit stored in advance as learning information,
The prediction server
A first step of counting the number of comments in each unit time in the initial motion determination time for the prediction target content acquired from the site server;
A second step of identifying a posting tendency from the transition state of the number of comments in the initial motion determination time in the prediction target content;
A third step of searching for content similar to the transition state of the number of comments of the initial motion determination time in the prediction target content from the same transition type in the learning information storage unit ;
Retrieved the comments transition state after that initial determination time corresponding to the content, and executes a fourth step of deriving a transition state of the future number of comments in the prediction target content prediction server number of comments prediction method of.

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