JP7537815B1 - Information Processing System - Google Patents

Abstract

[Problem] It takes time to analyze posted information, and it is difficult to perform effective analysis. [Solution] The information processing system 10 disclosed herein includes an acquisition unit 11 that acquires posted information posted about a specific store, a classification unit 12 that acquires classification results in which text included in the posted information is classified into a category according to the content of the text from among a plurality of preset categories, and an output unit 13 that outputs information based on the classification results. [Selected Figure] Figure 2

Description

This disclosure relates to an information processing system, an information processing method, and a program.

In recent years, a number of posting sites have been established on the Internet, which are websites that publish posted information that indicates users' evaluations of stores and products and their usage status. On such posting sites, for example, when users post information such as photos of the interior of a restaurant or other store and the food, and text describing the store's atmosphere and their impressions of using the store, the posted information is organized by store and by product and made available for general users to view. This allows general users to use the posted information as a reference when selecting a store or product.

Stores can also use the information posted by users as a reference for running their stores by collecting it. For example, Patent Literature 1 discloses a system that acquires information posted by users about the stores it operates from multiple posting sites and displays it as a list.

Patent No. 6984938

In the above-mentioned situation, the store can perform a detailed analysis of the collected posted information to further improve store operations. However, the volume of posted information is huge and the content is diverse, which creates problems in that the analysis takes time and makes it difficult to perform an effective analysis.

Therefore, the purpose of this disclosure is to solve the above-mentioned problem that analyzing posted information takes time and is difficult to perform effectively.

An information processing system according to an embodiment of the present disclosure includes:
An acquisition means for acquiring posted information posted about a predetermined store;
A classification means for acquiring a classification result obtained by classifying a sentence included in the posted information into a category corresponding to the content of the sentence among a plurality of preset categories;
an output means for outputting information based on the classification result;
Equipped with
The structure is as follows.
In addition, an information processing method according to an embodiment of the present disclosure includes:
An information processing device,
Obtaining posted information about a specific store,
Obtaining a classification result in which a sentence included in the posted information is classified into a category corresponding to the content of the sentence among a plurality of preset categories;
outputting information based on the classification result;
The structure is as follows.
In addition, a program according to an embodiment of the present disclosure includes:
Obtaining posted information about a specific store,
Obtaining a classification result in which a sentence included in the posted information is classified into a category corresponding to the content of the sentence among a plurality of preset categories;
outputting information based on the classification result;
Have a computer carry out the process,
The structure is as follows.

By configuring as described above, this disclosure makes it possible to analyze posted information quickly and effectively.

1 is a diagram illustrating an overall configuration of an information processing system according to the present disclosure. 2 is a block diagram showing a configuration of the management server disclosed in FIG. 1 . FIG. 2 is a diagram showing a process performed by the management server disclosed in FIG. 1; FIG. 2 is a diagram showing a process performed by the management server disclosed in FIG. 1; FIG. 2 is a diagram showing a process performed by the management server disclosed in FIG. 1 . FIG. 2 is a diagram showing a process performed by the management server disclosed in FIG. 1; 2 is a flowchart showing an operation of the management server disclosed in FIG. 1 .

<Embodiment 1>
A first embodiment of the present disclosure will be described with reference to Fig. 1 to Fig. 7. Fig. 1 and Fig. 2 are diagrams for explaining the configuration of an information processing system, and Fig. 3 to Fig. 7 are diagrams for explaining the processing operation of the information processing system.

[composition]
The information processing system of the present invention is mainly for managing information posted on a posting site, which is a so-called word-of-mouth site. As shown in Fig. 1, the information processing system is composed of a management server 10, a plurality of posting site servers 20, a poster terminal 30, a viewer terminal 40, an administrator terminal 50, and a text processing device 60, which are connected via a network N. Each component will be described in detail below.

The posting site server 20 is an information processing device managed by a business that provides a service for publishing posted information, and operates a website such as a posting site on the Internet. In this embodiment, the posting site provided by the posting site server 20 publishes a page that publishes information about a store P, such as a restaurant, as well as a page that publishes posted information from contributors 31, who are users who have used the store P. The posted information posted and published by contributors 31 includes, for example, photographs (photographed images) of the interior and food of store P (target), text information (character information) such as impressions of the service and food received at store P, and even quantified evaluation information expressing an evaluation of store P.

At this time, the evaluation information is a numerical evaluation value that indicates the degree of evaluation of the store by the user. As an example, the evaluation value is set to a value of "0 to 5", with the higher the numerical value, the higher the evaluation. However, the evaluation value may be expressed as a value in any range, and may be expressed by any information, not limited to numerical values. The evaluation information also includes time information. For example, the time information is information that indicates time, such as the date that the user used the store, or the date that the evaluation information was posted by the user. The time information is included in the evaluation information, for example, by being entered by the user together with the evaluation information at the time of posting, or by being added when the evaluation information is posted by the posting site server 20.

An example of a posting site provided by the posting site server 20 is Google (registered trademark) Business Profile. However, the posting site provided by the posting site server 20 may be any posting site, such as a search site, a reservation site, a review site, a survey site, a weblog, or a SNS (Social Networking Service) site. An example of an SNS site is Instagram (registered trademark).

In addition, in this embodiment, there are multiple posting site servers 20, each of which has a different posting site. Therefore, posting information for the same store P is posted and made public on each posting site opened by each posting site server 20.

In addition, the posting site server 20 is not necessarily limited to publishing the posted information on a website or the like. For example, the posting site server 20 may simply set up a survey site or a payment site and acquire and store the posted information described above from users.

The posted information handled by the posting site provided by the posting site server 20 is not limited to information about a store P such as a restaurant, but may be information about any type of store, or may be posted information about a product or service (target). Furthermore, the posted information is not limited to information about a store or product, but may be information about any target. The posting site server 20 is not necessarily limited to discriminating between posted information by target, such as by store or by product, but may disclose posted information without discriminating between targets, for example, by poster 31.

The poster terminal 30 (user terminal) is an information processing terminal such as a smartphone or laptop computer operated by a poster 31, who is a user who has used an object such as store P. The poster terminal 30 has a function of accessing a posting site on the Internet when operated by the poster 31 and posting posted information to the posting site. For example, the poster terminal 30 posts photos taken by the poster 31 inside store P or photos of food served at store P as posted information, and posts evaluation information entered by the poster 31 that evaluates store P on a multiple-level numerical scale or text information expressing the poster's impressions in text as posted information.

The poster terminal 30 may directly access the posting site, or may be directed to access the posting site from an access destination based on address information such as a QR code installed in store P. In addition, if the access destination of address information such as a QR code installed in store P is a survey site for store P, the poster terminal 30 also has a function of transmitting responses to surveys presented on the survey site.

The viewer terminal 40 is an information processing terminal such as a smartphone or a laptop computer operated by the viewer 41. The viewer terminal 40 has a function of accessing a posting site on the Internet when operated by the viewer 41 and viewing posted information made public on the posting site. For example, the viewer terminal 40 can view photos of the inside of store P and photos of the food served at store P posted by the poster 31, evaluation information that rates store P on a multiple-level numerical scale, and text information expressing impressions in text.

The administrator terminal 50 is an information processing terminal operated by an administrator 51 who manages the posted information of the store at a business operator who operates the store. The administrator terminal 50 accesses the management server 10 and manages the posted information for the store, such as displaying and monitoring the posted information of the store acquired by the management server 10 as described below, and analyzing the posted information. In particular, the administrator terminal 50 acquires a classification result that is classified into items that are set in advance according to the contents of the text information (split text information) of the posted information, as described below, and displays the classification result to analyze the posted information. The administrator terminal 50 also generates a reply text to the posted information via the management server 10 and posts it to the posting site server 20. The administrator terminal 50 also manages information such as a questionnaire about the store acquired by the management server 10. Furthermore, the administrator terminal 50 can also post information about the store on each posting site via the management server 10. Note that the number of stores for which the administrator 51 manages posted information is not limited to one, and may be multiple.

In this embodiment, the business operator who operates store P operates multiple stores P. For example, the business operator operates 50 stores, and monitors and analyzes the posted information for these stores, and also posts reply messages to the posted information. However, the business operator who operates store P may only operate one store, or may monitor and analyze the posted information for multiple stores, including its own store and stores of other businesses.

The text processing device 60 (text processing device) is an information processing device that is connected to the management server 10 via the network N, performs requested processing on input text information, and outputs the processing results to the management server 10 and is managed by a business that provides such services. As an example, the text processing device 60 is realized by an artificial intelligence chatbot such as ChatGPT provided by OpenAI, Inc. in the United States or VertexAI provided by Google LCC in the United States.

In this embodiment, the text processing device 60 implements a text segmentation processing model generated by machine learning. The text segmentation processing model is generated by machine learning learning data consisting of a combination of text information and divided text information obtained by dividing the text information according to its content, which is prepared in advance. As a result, the text segmentation processing model is configured to divide the text information into divided text information consisting of character strings of shorter length in response to a request for dividing the text information. However, the text processing device 60 may divide the text information into multiple divided text information by any method without using the text segmentation processing model. For example, the text processing device 60 may perform morphological analysis of character strings in the text information to generate divided text information according to the content of the text according to the order of morphemes and the connection status, or may generate literary text information by dividing the text information by punctuation marks or paragraphs.

In addition, in this embodiment, the text processing device 60 implements a text classification processing model generated by machine learning. The text segmentation processing model is generated by machine learning learning data consisting of a combination of pre-prepared text information (split text information) and items corresponding to the content of the text information (split text information). As a result, the text classification processing model is configured to classify the text information into an item corresponding to the content of the text information from among a plurality of pre-set items in response to a request for classification processing of the text information. Note that in this embodiment, the text classification processing model is configured to classify each piece of split text information split from the text information by the text segmentation processing model as described above into an item corresponding to the content of the split text information.

Here, in the text classification processing model, four contents are set as classification items: "quality", "customer service", "cleanliness", and "atmosphere". The item "quality" is the quality of the store (Quality (Q)), and for example, represents the quality of the products (e.g., food) provided in the store, the menu, the price, and other quality-related contents. The item "customer service" is the service (Service (S)) in the store, and for example, represents the service-related contents such as the customer service and attention to detail in the store. The item "cleanliness" is the sanitary condition (Cleanliness (C)) in the store, and for example, represents the sanitary aspects such as the cleanliness, cleaning status, and handling of products in the store. The item "atmosphere" is the atmosphere (Atmosphere (A)) in the store, and for example, represents the environmental contents such as the exterior and interior according to the concept of the store, and the uniforms of the employees. The text classification processing model then analyzes the contents of the divided text information described above, and classifies them into the items corresponding to such contents: "quality", "customer service", "cleanliness", and "atmosphere".

In addition, in the text classification processing model of this embodiment, in addition to the four items mentioned above, items for "positive" and "negative" content are set. In other words, the text classification processing model classifies the divided text information into items of "positive" and "negative" according to the content of the text. For this reason, the text classification processing model classifies one piece of divided text information into one of the four items of "quality," "customer service," "cleanliness," and "atmosphere," and further classifies it into one of two items of "positive" and "negative." Here, "positive" represents positive content about the store, and "negative" represents negative content about the store. The text classification processing model then analyzes the content of the divided text information mentioned above, and classifies it into the items corresponding to such content, "positive" and "negative."

However, the text processing device 60 may classify the divided text information into the above-mentioned items in any manner without using a text classification processing model. For example, the text processing device 60 may have the text content, phrases, keywords, etc. corresponding to each item set in advance, and may classify the divided text information into corresponding items according to the text content, phrases, keywords in the text, etc.

In addition, in this embodiment, the text processing device 60 implements a program generation model generated by machine learning. The program generation model is generated by machine learning learning data consisting of a combination of text information (split text information) and items prepared in advance, and program data with contents associated with these. As a result, the program generation model is configured to perform a process of generating program data that associates text information with items into which the text information is classified, as described above, in response to a request for program generation processing of text information. Note that in this embodiment, the program generation model performs a process of generating program data that associates split text information split from text information by the text split processing model with items into which the split text information is classified, as described above. As an example, the program generation model generates program data that associates split text information with classified items (one of "quality", "customer service", "cleanliness", and "atmosphere" and one of "positive" and "negative") in a programming language in JSON (JavaScript Object Notation) format, as shown in FIG. 3 described later. However, the format of the program generated by the program generation model can be in any format or language.

However, the text processing device 60 may generate program data in any manner without using a program generation model. For example, the text processing device 60 may have rules set for converting associated divided text data and items into program data or may have templates for program data set, and may use such rules or templates to convert associated divided text data and items into program data.

The management server 10 is composed of one or more information processing devices each having a calculation device and a storage device. As shown in FIG. 2, the management server 10 is equipped with an acquisition unit 11, an analysis unit 12, and an output unit 13. The functions of the acquisition unit 11, the analysis unit 12, and the output unit 13 can be realized by the calculation device executing a program for realizing each function stored in the storage device. The management server 10 is also equipped with a store information storage unit 16 and a posted information storage unit 17. The store information storage unit 16 and the posted information storage unit 17 are composed of a storage device. Each component will be described in detail below.

First, the store information storage unit 16 stores information about the store P input from the administrator terminal 50 of the business operator who operates the store P. At this time, if the business operator operates multiple stores P, the information about each of the multiple stores P is registered.

Store information to be registered includes the store name, store address, store attributes, and information on the posting site server 20 to which posted information for the store is posted. The store name functions as identification information to identify the store. The store address indicates the location of the store, and can specify the location by prefecture or region, for example, and also functions as an attribute of the store. Store attributes include information on the store's location (roadside, downtown, tourist spot, suburban, commercial facility, etc.), whether or not there is parking, the store's person in charge on the business side, the store's franchisee, the store's brand, and so on. The information on the posting site server 20 to which the posted information for the store is posted is information to identify the posting site, or information for obtaining posted information posted to the posting site server 20 from the posting site server 20. For example, it is information to identify the posting site server 20 and the store, and one example is address information of the posting site where posted information for the store is published.

In addition, in the store information storage unit 16, for example, multiple stores operated by the same business operator or businesses that form a group are associated with each other to form a store group and registered.

The acquisition unit 11 (acquisition means) accesses each posting site server 20 based on the registered store information as described above, acquires the posted information published on each posting site, and stores it in the posted information storage unit 17. At this time, the acquisition unit 11 acquires posted information related to each store from each posting site server 20, distinguishing between each store and each store group, associates the store information and the posting site information, and stores it in the posted information storage unit 17. In other words, the acquired posted information is associated with the corresponding store information and the information of the posting site where the information was posted. The acquisition unit 11 may acquire posted information from any posting site, such as the above-mentioned search sites, reservation sites, review sites, survey sites, web logs, and SNS sites.

The analysis unit 12 (classification means) requests the text processing device 60 to perform an analysis process of the text information included in the posted information acquired as described above. Specifically, the analysis unit 12 transmits the text information included in the posted information to the text processing device 60, and requests the text information to be divided, classified, and generated as a program. In response to this, the text processing device 60 first divides the text information into divided text information using a text division processing model, as described above, and classifies each divided text information into items according to the contents of the divided text information using a text classification processing model. Furthermore, the text processing device 60 generates program data that associates the divided text information with the items into which the divided text information is classified using a program generation model. Then, the analysis unit 12 acquires the program data generated by the text processing device 60. In other words, the analysis unit 12 acquires a classification result in which each divided text information obtained by dividing the text information included in the posted information is classified into a preset item, and acquires program data that associates the divided text information with the items that are the classification result.

The analysis unit 12 may sequentially obtain the above-mentioned processing results from the text processing device 60, or may sequentially make the above-mentioned processing requests to the text processing device 60. That is, the analysis unit 12 may first make a request for segmentation processing of the text information to obtain the segmented text information, then make a request for classification processing of the segmented text information to obtain the classification results, and further make a request for program generation to obtain the program data.

Now, referring to FIG. 3, the text information included in the posting information for which the analysis unit 12 requests the text processing device 60 to perform an analysis process, and an example of the analysis process by the text processing device 60 will be described. First, assume that the text information included in the posting information is, as shown in reference D1 of FIG. 3, "You can eat delicious yakiniku for 1000 yen! The staff is a bit unfriendly and scary, but the floor is slippery, so be careful not to fall. The atmosphere is calm and comfortable." The text processing device 60 divides the text information shown in reference D1 of FIG. 3 into each divided text information shown in each line of reference D2 of FIG. 3, and further classifies the divided text information into items shown at the left end of each line according to the content of the divided text information. For example, the divided text information "You can eat delicious yakiniku for 1000 yen!" shown in the first line of reference D2 of FIG. 3 is classified into the item "quality (Q)" and the item "positive (positive)" according to its content. In addition, the divided sentence information shown in the second line, "The staff are a bit unfriendly and scary though," is classified into the category "Service (S)" and the category "Negative" depending on its content. In addition, the divided sentence information shown in the third line, "Also, be careful not to fall as the floor is slippery," is classified into the category "Cleanliness (C)" and the category "Positive" depending on its content. In addition, the divided sentence information shown in the fourth line, "The atmosphere is calm and comfortable," is classified into the category "Atmosphere (A)" and the category "Positive" depending on its content.

The text processing device 60 then generates program data that associates the divided text information shown in D2 of FIG. 3 with the classified items, as shown in D3 of FIG. 3. For example, the divided text information shown in the first line of D2 of FIG. 3, "You can eat delicious yakiniku for 1000 yen!", with the items "quality (Q)" and "positive", to generate program data like the first paragraph of D3 of FIG. 3. In the program data, the divided text information is shown as "text information" following "sentence", and the items are shown as "quality", "service", "cleanliness" or "atmosphere" following "category", and further items are shown as "positive" or "negative" following "sentiment".

The analysis unit 12 acquires the program data shown by reference symbol D3 in FIG. 3 from the text processing device 60, but may also acquire information on each divided text that is obtained by dividing text information, or information that associates the divided text information with items that are classification results, as shown by reference symbol D2.

Although the analysis unit 12 requests the text processing device 60 to perform an analysis process of the text information and obtains the processing result by the text processing device 60, the analysis unit 12 itself may perform the analysis process of the text information described above. For example, the analysis unit 12 may be equipped with each model provided in the text processing device 60 described above, and may perform the above-mentioned division process, classification process, and program generation process using such models. Alternatively, the analysis unit 12 may divide the text information, classify the divided text information, and generate program data according to the content of the text information. For example, the analysis unit 12 may generate divided text information according to the result of morphological analysis of the text information, classify the divided text information into items according to keywords included in the divided text information, and generate a program that associates the divided text information with the classified items using a template.

The output unit 13 outputs the posted information for a specific store acquired from the posting site server 20 as described above for display on the administrator terminal 50. At this time, when there is a plurality of pieces of posted information corresponding to a specific store acquired from a plurality of posting site servers 20, the output unit 13 outputs the plurality of pieces of posted information so as to be displayed together in a list. Furthermore, when a plurality of specific stores are associated with each other to form a store group and registered as described above, the output unit 13 outputs the posted information corresponding to each of the plurality of specific stores so as to be displayed together in a list. The output unit 13 associates the posted information with information such as the store name that identifies the store where the posted information was posted, information such as the posting site name that identifies the posting site, and information about the poster, and displays the information in association with the posted information.

For example, as shown in FIG. 4, the output unit 13 outputs a list of evaluation information and text information from the posted information to be displayed on the administrator terminal 50. The example in FIG. 4 shows a case where "AAA Store" and "BBB Store" are associated as group stores, and the output unit 13 displays a list of evaluation information and text information as posted information posted to multiple posting sites such as "Posting Site A" and "Posting Site B" for each of these multiple stores. Note that in FIG. 4, "stars" represent evaluation information, and the number of stars represents a numerical evaluation value, with a higher number indicating a higher evaluation of the store. Also, in FIG. 4, the output unit 13 displays text information such as impressions about the store included in the evaluation information, but if the posted information includes photographed images such as photos, the output unit 13 may display a list of the photographed images.

Then, when displaying the above-mentioned posted information, the output unit 13 outputs information corresponding to the processing result acquired from the text processing device 60. Specifically, the output unit 13 outputs the divided text information so as to associate and display the item into which the divided text information is classified. As an example, as shown in FIG. 4, the output unit 13 displays the divided text information "You can eat delicious yakiniku for 1000 yen!" in association with the item "quality (Q)" and the item "positive", and also displays the divided text information "The waiter is a bit unfriendly and scary though" in association with the item "service (S)" and the item "negative". At this time, the output unit 13 can easily display the divided text information in association with the item into which the divided text information is classified by using the program data acquired from the text processing device 60.

In addition, the output unit 13 may output information on the items into which the divided text information acquired as described above is classified to the administrator terminal 50 for each store or for each attribute set for the store. At this time, the output unit 13 may tally up the items into which the divided text information for the store is classified for each store or for each attribute set for the store, and output information based on the tallying results to be displayed. For example, as shown in FIG. 5, the output unit 13 may tally up the number of divided text information for the store and the number of items into which the divided text information is classified for each store, calculate the ratio of the number of items to the number of all the divided text information, and display the ratio for each item. As an example, in FIG. 5, the tallying results for "AAA store" are displayed in the second row, and a radar chart with four items, "quality," "customer service," "cleanliness," and "atmosphere," is displayed as vertices, and the calculated ratio of the number of divided text information classified into each item is displayed on the radar chart, and the numerical value of the ratio is also displayed. In particular, this example shows the percentage of segmented text information classified as "positive" out of all the items classified as "quality," "customer service," "cleanliness," and "atmosphere." The first row of Figure 5 shows the aggregated results for "all stores," which allows comparison with the average value.

In addition, FIG. 6 shows an example in which, for each store attribute, the number of divided text information for all stores belonging to the corresponding attribute and the number of items into which the divided text information is classified are tallied, the ratio of the number of items to the total number of divided text information is calculated, and the ratio of each item is displayed. As an example, FIG. 6 shows an example in which data is tallied by the store attribute "brand," and the second row displays the results of the tallies for 10 stores belonging to "brand AA." In particular, this example displays the ratio of the number of divided text information classified into the item "positive" out of those classified into the items "quality," "customer service," "cleanliness," and "atmosphere." The first row of FIG. 6 displays the results of the tallies for "all stores," which allows comparison with the average value. It is also possible to tally up data by any store attribute, such as the store's "region (prefecture)" or "location," and display the results of the tallies.

In the above, the output unit 13 displays, in a graph, the ratio of the number of items for each category to the number of all the divided text information items. However, the output unit 13 may simply display, in a graph, the number of items into which the divided text information items are classified. For example, the output unit 13 may display, in a graph or numerical form, the number of items classified into the categories "quality," "customer service," "cleanliness," and "atmosphere" or the number of items classified into the categories "positive" and "negative" for each store or each store attribute. Furthermore, the output unit 13 may display any information in any display form, as long as the information is based on the number of items into which the divided text information items are classified.

[Action]
Next, the operation of the above-mentioned information processing system, particularly the operation of the management server 10, will be described mainly with reference to the flowchart of FIG.

First, on each of the multiple posting site servers 20, posting information about store P is posted by a contributor 31 and made public, and can be viewed from a viewer terminal 40 of a viewer 41.

The management server 10 periodically or at any time acquires posted information published on each posting site from the multiple posting site servers 20 (step S1). The management server 10 then stores the posted information acquired from the multiple posting site servers 20 for each store or for each store group.

Then, the management server 10 requests the text processing device 60 to analyze the text information contained in the acquired posting information (step S2). Specifically, the management server 10 transmits the text information contained in the posting information to the text processing device 60, and requests the text information to be divided, classified, and generated as a program. In response to this, the text processing device 60 divides the text information into split text information as shown in FIG. 3, classifies each piece of split text information into categories according to the contents of the split text information, and generates program data that associates the split text information with the categories into which the split text information is classified. The management server 10 then obtains the analysis results by the text processing device 60, that is, the split text information and the classification results into categories as shown in FIG. 3, and the program data (step S3).

Next, the management server 10 outputs the posted information to be displayed on the administrator terminal 50. At this time, the management server 10 outputs the analysis result of the text information included in the posted information to be displayed (step S4). For example, as shown in FIG. 4, when displaying a list of the text information of the posted information, the management server 10 displays the divided text information into which the text information is divided, and outputs the items into which each divided text information is classified in association with each other. In addition, as shown in FIG. 5 and FIG. 6, the management server 10 may output the items into which the divided text information for each store is classified for each store or for each attribute set for the store, and display information based on the results of the aggregation. As an example, a radar chart with four items, "quality," "customer service," "cleanliness," and "atmosphere," as vertices for each store or for each attribute of the store may be displayed, and the calculated percentage of the number of divided text information classified into each item may be displayed on the radar chart.

As described above, the information processing system in this embodiment divides sentences included in posted information, obtains classification results in which each divided sentence is classified into categories according to its content, and outputs information based on the classification results. For example, the division process and classification process can be performed using a model generated by machine learning. As a result, in this embodiment, even if the number of posted information is huge and the content is diverse, it is possible to shorten the time required for analysis and perform effective analysis.

In the above example, the text information included in the posted information is divided into pieces of divided text information, and each piece of divided text information is classified into categories. However, the text information may be classified into categories without being divided.

Although the present invention has been described above with reference to the above-mentioned embodiments, the present invention is not limited to the above-mentioned embodiments. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. Furthermore, at least one or more of the functions possessed by the management server 10 described above may be executed by an information processing device installed and connected anywhere on the network, that is, they may be executed by so-called cloud computing.

The above-mentioned program can be stored and supplied to a computer using various types of non-transitory computer readable media. Non-transitory computer readable media include various types of tangible storage media. Examples of non-transitory computer readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R/Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer readable media can supply the program to a computer via wired communication paths such as electric wires and optical fibers, or wireless communication paths.

<Additional Notes>
A part or all of the above-described embodiments can be described as follows: The following provides an overview of the configurations of an information processing system, an information processing method, and a program according to the present invention. However, the present invention is not limited to the following configurations.
(Appendix 1)
An acquisition means for acquiring posted information posted about a predetermined store;
A classification means for acquiring a classification result obtained by classifying a sentence included in the posted information into a category corresponding to the content of the sentence among a plurality of preset categories;
an output means for outputting information based on the classification result;
Equipped with
Information processing system.
(Appendix 2)
2. The information processing system according to claim 1,
the classification means acquires the classification result obtained by classifying each divided sentence obtained by dividing the sentence into the category corresponding to the content of each divided sentence,
the output means outputs information based on the classification result for each of the divided sentences.
Information processing system.
(Appendix 3)
3. The information processing system according to claim 2,
the classification means transmits the text to a text processing device, requests a process of dividing the text and a process of classifying each of the divided texts, and obtains the classification results for each of the divided texts as a result of the process of dividing the text and the process of classifying the divided text performed by the text processing device.
Information processing system.
(Appendix 4)
3. The information processing system according to claim 2,
the output means outputs the divided sentences so as to be associated with the categories into which the divided sentences are classified.
Information processing system.
(Appendix 5)
3. The information processing system according to claim 2,
the classification means acquires program data associating the divided sentences with the categories into which the divided sentences are classified;
the output means outputs the associated divided sentences and the items on a display device in association with each other based on the program data;
Information processing system.
(Appendix 6)
3. The information processing system according to claim 2,
the classification means acquires the classification result in which each of the divided sentences is classified into the category corresponding to the content of the divided sentence among the set categories of quality, customer service, cleanliness, and atmosphere of the store;
Information processing system.
(Appendix 7)
7. The information processing system according to claim 6,
The classification means further acquires the classification result in which each of the divided sentences is classified into the item corresponding to the content of the divided sentence among the items set to affirmative or negative.
Information processing system.
(Appendix 7)
7. The information processing system according to claim 6,
The output means outputs, for each of the stores, information based on the number of items into which each of the divided sentences of the sentence included in the posted information for the store is classified.
Information processing system.
(Appendix 9)
7. The information processing system according to claim 6,
The output means outputs, for each attribute set for the store, information based on the number of items into which each of the divided sentences of the sentence included in the posted information for the store belonging to the attribute is classified.
Information processing system.
(Appendix 10)
An information processing device,
Obtaining posted information about a specific store,
Obtaining a classification result in which a sentence included in the posted information is classified into a category corresponding to the content of the sentence among a plurality of preset categories;
outputting information based on the classification result;
Information processing methods.
(Appendix 11)
Obtaining posted information about a specific store,
Obtaining a classification result in which a sentence included in the posted information is classified into a category corresponding to the content of the sentence among a plurality of preset categories;
outputting information based on the classification result;
A program that causes a computer to carry out processing.

REFERENCE SIGNS LIST 10 Management server 11 Acquisition unit 12 Analysis unit 13 Output unit 16 Store information storage unit 17 Post information storage unit 20 Posting site server 30 Contributor terminal 31 Contributor 40 Viewer terminal 41 Viewer 50 Administrator terminal 51 Administrator 60 Text processing device P Store

Claims (11)

An acquisition means for acquiring posted information posted about a predetermined store;
A classification means for acquiring a classification result obtained by classifying a sentence included in the posted information into a category corresponding to the content of the sentence among a plurality of preset categories;
an output means for outputting information based on the classification result;
Equipped with
the classification means acquires the classification result obtained by classifying each divided sentence obtained by dividing the sentence into the category corresponding to the content of each divided sentence,
the output means outputs information based on the classification result for each of the divided sentences;
Furthermore, the output means outputs, as the store information, information based on the number of items into which each of the divided sentences of the sentence included in the posted information for the store belonging to a specific store attribute among the store attributes set for the store, and information based on the number of items into which each of the divided sentences of the sentence included in the posted information for all the stores.
Information processing system. 2. The information processing system according to claim 1,
The output means outputs, for the specific store attribute, a ratio of the number of items into which each of the divided sentences is classified to the number of all the divided sentences included in the posted information for the store belonging to the specific store attribute, and a ratio of the number of items into which each of the divided sentences is classified to the number of all the divided sentences included in the posted information for all the stores, for all the stores.
Information processing system. 2. The information processing system according to claim 1,
The output means outputs so as to simultaneously display, for the specific store attribute, a ratio of the number of the items into which each of the divided sentences is classified to the number of all the divided sentences included in the posted information for the store belonging to the specific store attribute, and a ratio of the number of the items into which each of the divided sentences is classified to the number of all the divided sentences included in the posted information for all the stores, for all the stores.
Information processing system. 2. The information processing system according to claim 1,
The output means outputs, for the specific store attribute, a ratio of the number of items into which each of the divided sentences is classified to the number of all of the divided sentences included in the posted information for the store belonging to the specific store attribute, so as to be displayed on a radar chart with each of the items as vertices, and outputs, for all of the stores, a ratio of the number of items into which each of the divided sentences is classified to the number of all of the divided sentences included in the posted information for all of the stores, so as to be displayed on a radar chart with each of the items as vertices.
Information processing system. 2. The information processing system according to claim 1,
The store attribute is any one of the location of the store, the brand of the store, and the location of the store.
Information processing system. 2. The information processing system according to claim 1 ,
the classification means transmits the text to a text processing device, requests a process of dividing the text and a process of classifying each of the divided texts, and obtains the classification results for each of the divided texts as a result of the process of dividing the text and the process of classifying the divided text performed by the text processing device.
Information processing system. 2. The information processing system according to claim 1 ,
the output means outputs the divided sentences so as to be associated with the categories into which the divided sentences are classified.
Information processing system. 2. The information processing system according to claim 1 ,
the classification means acquires program data associating the divided sentences with the categories into which the divided sentences are classified;
the output means outputs the associated divided sentences and the items on a display device in association with each other based on the program data;
Information processing system. 2. The information processing system according to claim 1 ,
the classification means acquires the classification result in which each of the divided sentences is classified into the category corresponding to the content of the divided sentence among the set categories of quality, customer service, cleanliness, and atmosphere of the store;
Information processing system. An information processing device,
Obtaining posted information about a specific store,
Obtaining a classification result in which each divided sentence obtained by dividing the sentence included in the posted information is classified into a category corresponding to the content of each divided sentence from among a plurality of preset categories;
When outputting information based on the classification results for each of the divided sentences , information based on the number of items into which each of the divided sentences of the sentence included in the posted information for a store that belongs to a specific store attribute among the store attributes set for the store as information about the store is output, and information based on the number of items into which each of the divided sentences of the sentence included in the posted information for all of the stores is output.
Information processing methods. Obtaining posted information about a specific store,
Obtaining a classification result in which each divided sentence obtained by dividing the sentence included in the posted information is classified into a category corresponding to the content of each divided sentence from among a plurality of preset categories;
When outputting information based on the classification results for each of the divided sentences , information based on the number of items into which each of the divided sentences of the sentence included in the posted information for a store that belongs to a specific store attribute among the store attributes set for the store as information about the store is output, and information based on the number of items into which each of the divided sentences of the sentence included in the posted information for all of the stores is output.
A program that causes a computer to carry out processing.