Analysis support server, analysis support method and program

Abstract

To provide technology for assisting a user in efficiently analyzing company information.SOLUTION: An analysis-assisting server for providing information for assisting in analysis of companies comprises an acquisition unit that obtains a search keyword specified by a user; an identification unit that identifies one or more records including the search keyword from multiple records read from multiple data sources and identifies one or more companies from the one or more records; and a display unit that displays a list indicating the identified one or more companies.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to an analysis support server, an analysis support method, and a program. [Background technology]

[0002] There is known a technique for analyzing texts about companies in order to consider investment targets. Patent Document 1 describes a technique for calculating a corporate evaluation value of a target company based on evaluation values ​​of keywords included in evaluation texts for each of a plurality of companies. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2007-183796 A Summary of the Invention [Problem to be solved by the invention]

[0004] Information about a company, i.e., company information, may be collected from various data sources. If the company information collected from the various data sources is presented separately for each data source, it may be difficult for a user to efficiently analyze the company information. Some aspects of the present invention aim to provide a technique for supporting a user to efficiently analyze company information. [Means for solving the problem]

[0005] According to some embodiments, there is provided an analysis support server for providing information to assist in the analysis of companies, the analysis support server comprising: an acquisition means for acquiring a search keyword specification from a user; an identification means for identifying one or more records including the search keyword from a plurality of records read from a plurality of data sources and identifying one or more companies from the one or more records; and a display means for displaying a list showing the one or more identified companies. Effect of the Invention

[0006] Some embodiments may assist users in efficiently analyzing business information. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram illustrating an example of the configuration of a system according to some embodiments. [Diagram 2] FIG. 1 is a block diagram illustrating an example of a hardware configuration of a computer according to some embodiments. [Diagram 3] FIG. 4 is a schematic diagram illustrating various records according to some embodiments. [Figure 4] FIG. 11 is a flow diagram illustrating an example method for generating a classification record according to some embodiments. [Diagram 5] FIG. 4 is a flow diagram illustrating an example method for searching company information according to some embodiments. [Figure 6] FIG. 11 is a schematic diagram illustrating an example of a display screen for search results according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the invention according to the claims, and not all combinations of features described in the embodiments are essential to the invention. Two or more features among the multiple features described in the embodiments may be arbitrarily combined. In addition, the same reference numbers are used for the same or similar configurations, and duplicated descriptions are omitted.

[0009] With reference to FIG. 1, a configuration example of a system including an analysis support server 100 according to some embodiments will be described. The analysis support server 100 is a server that performs processing to support a user in analyzing a company. Details of this processing will be described later. The analysis support server 100 can communicate with one or more database (DB) servers through a network 107. For example, the analysis support server 100 may be able to communicate with a company DB server 101, an investor DB server 102, a literature DB server 103, an intellectual property DB server 104, and a clinical trial DB server 105. Each DB server provides information managed by itself to the analysis support server 100 in response to a request from the analysis support server 100. In this way, each DB server functions as a data source. The analysis support server 100 may use only a part of these DB servers as data sources, or may use other DB servers as additional data sources.

[0010] The company DB server 101 is a server that manages a DB that stores general information about companies. The investor DB server 102 is a server that manages a DB that stores information about investors. An investor may be a legal entity such as a company, or may be a natural person. The literature DB server 103 is a server that manages a DB that stores information about literature. For example, the literature DB server 103 may store information about technical literature. The technical literature may include academic literature such as papers. The intellectual property DB server 104 is a server that manages a DB that stores information about intellectual property. The intellectual property may include patents, designs, trademarks, etc. The clinical trial DB server 105 is a server that manages a DB that stores information about clinical trials.

[0011] The analysis support server 100 may further be capable of communicating with a client terminal 106 through a network 107. The client terminal 106 may be used by a user to utilize an analysis support service provided by the analysis support server 100. For example, the analysis support server 100 may provide the analysis support service through a web application. The client terminal 106 may access the web application of the analysis support server 100 using its own browser or a dedicated application. The client terminal 106 may be, for example, a personal computer, a smartphone, a tablet, or the like.

[0012] The network 107 is any network that provides the analysis support server 100 with communication with other devices (DB servers and client terminals 106). For example, the network 107 may include a local area network (LAN), a wide area network such as the Internet, or an internal network within a cloud system.

[0013] With reference to FIG. 2, an example of the hardware configuration of the computer 200 will be described. The computer 200 may include the components shown in FIG. 2. The computer 200 may not include some of the components shown in FIG. 2, or may include components not shown in FIG. 2. The computer 200 may be used as any of the analysis support server 100, the company DB server 101, the investor DB server 102, the literature DB server 103, the intellectual property DB server 104, the clinical trial DB server 105, and the client terminal 106. The multiple servers shown in FIG. 1 may be realized by separate computers 200. Alternatively, some of the multiple servers shown in FIG. 1 may be realized by a single computer 200.

[0014] The processor 201 is a device that controls the overall operation of the computer 200. The processor 201 functions as, for example, a CPU (Central Processing Unit). The memory 202 is a device that stores programs and temporary data necessary for the operation of the computer 200. The memory 202 is composed of, for example, a RAM (Random Access Memory) and a ROM (Read Only Memory). The operation of the computer 200 may be performed, for example, by the processor 201 executing a program stored in the memory 202. Alternatively, some or all of the operation of the computer 200 may be performed by a dedicated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0015] The input device 203 is a device for obtaining input from a user of the computer 200. The input device 203 is constituted by, for example, a keyboard and a mouse. The output device 204 is a device for outputting to the user of the computer 200. The output device 204 is constituted by, for example, a display and a speaker. The communication device 205 is a device for connecting the computer 200 to the network 107. The communication device 205 is constituted by, for example, a network interface card (NIC) and a wireless communication module.

[0016] The storage device 206 is a device that stores data used for the operation of the computer 200. The storage device 206 is configured by a storage medium such as a hard disk drive (HDD), a solid state drive (SSD), or a digital versatile disk (DVD).

[0017] With reference to FIG. 3, information managed by the analysis support server 100 and DB servers 101 to 105 will be described. The corporate DB server 101 manages a corporate record 301 for each company. In general, the corporate DB server 101 manages a plurality of corporate records 301. A corporate record 301 is a record that represents information about an individual company. The plurality of corporate records 301 may be managed in a table format or in another format. The same applies to the other records described below.

[0018] Company record 301 has the items shown in FIG. 3. Company record 301 may not include some of the items shown in FIG. 3, or may include other items. The same applies to the other records described below. The description of company record 301 is a sentence that describes the company, and is written in natural language. The keywords of company record 301 are one or more words that represent the company. Information for each item of company record 301 may be registered by an administrator who manages company DB server 101.

[0019] The investor DB server 102 manages an investor record 302 for each investor. In general, the investor DB server 102 manages a plurality of investor records 302. The investor record 302 is a record that represents information about an individual investor. Information for each item of the investor record 302 may be registered by an administrator who manages the investor DB server 102. The investor record 302 may include, for example, the name of the investor. The investor name may be the name of a natural person or a legal entity (e.g., a company) that makes an investment.

[0020] The document DB server 103 manages a document record 303 for each document. In general, the document DB server 103 manages a plurality of document records 303. A document record 303 is a record for managing information about an individual document. Information about each item of the document record 303 may be registered by an administrator who manages the document DB server 103. The document record 303 may include, for example, an author name and an affiliation name. The author name may be the name of the natural person who created the document, and the affiliation name may be the name of the company to which this natural person belongs.

[0021] The intellectual property DB server 104 manages an intellectual property record 304 for each intellectual property right case. In general, the intellectual property DB server 104 manages a plurality of intellectual property records 304. The intellectual property record 304 is a record for managing information on an individual case related to an intellectual property right (e.g., a patent application case, a design registration application case, etc.). Information on each item of the intellectual property record 304 may be registered by an administrator who manages the intellectual property DB server 104. The intellectual property record 304 may include, for example, the name of the applicant and the name of the patent owner. The applicant may be the name of a natural person or a legal entity (e.g., a company) that has filed an application. The right owner name may be the name of a natural person or a legal entity (e.g., a company) that holds the intellectual property right.

[0022] The clinical trial DB server 105 manages a clinical trial record 305 for each clinical trial. In general, the clinical trial DB server 105 manages multiple clinical trial records 305. A clinical trial record 305 is a record for managing information of an individual clinical trial. Information for each item of the clinical trial record 305 may be registered by an administrator who manages the clinical trial DB server 105. The clinical trial record 305 may include, for example, the name of the implementer, the name of the sponsor, and the name of the collaborator. The name of the implementer may be the name of a company that implements the clinical trial. The name of the sponsor may be the name of a company that invests in the clinical trial. The name of the collaborator may be the name of a company that cooperates in the clinical trial.

[0023] The analysis support server 100 manages the classification record 300 for each company. In general, the analysis support server 100 manages a plurality of classification records 300. The classification record 300 is a record for managing the classification of an individual company. Information on each item of the classification record 300 may be registered by an administrator who manages the analysis support server 100, or may be set by the analysis support server 100. The analysis support server 100 may manage the classification record 300 for all of the multiple company records 301 managed by the company DB server 101, or may manage the classification record 300 for some of the multiple company records 301 managed by the company DB server 101. For example, the analysis support server 100 may manage the classification of companies related to the healthcare and medical fields among the multiple company records 301 managed by the company DB server 101. One classification record 300 corresponds to any one of the company records 301.

[0024] The company record ID of the classification record 300 is identification information for uniquely identifying the company record 301 managed by the company DB server 101. The company record ID may be the company name included in the company record 301, or the legal entity name included in the company record 301. Alternatively, in the case where unique identification information is assigned to each company record 301 by the company DB server 101, the company record ID may be this identification information.

[0025] The analysis support server 100 assigns each company to one of a plurality of segments. In other words, the analysis support server 100 classifies a plurality of companies, whose information is obtained from a plurality of data sources (e.g., DB servers 101 to 105), into a plurality of segments. The analysis support server 100 may classify each company into a plurality of segments at a single stage, or may classify each company into a plurality of segments in a hierarchical structure with a plurality of stages.

[0026] In some embodiments, the analysis support server 100 may classify each company in a four-level hierarchical structure (e.g., sector, segment, tech segment, detailed segment). In this example, the sector is the top level, the next level is the segment, the next level is the tech segment, and the bottom level is the detailed segment. These four-level hierarchical structures may have a tree structure. Specifically, each sector may be divided into multiple segments, each segment may be divided into multiple tech segments, and each tech segment may be divided into multiple detailed segments.

[0027] The highest level of the hierarchy, sector, represents a classification of companies by business model. For example, a sector may have classifications such as B2C (business to customer) and B2B (business to business).

[0028] The second level, segments, represent the classification of technology areas that each company strategically focuses on within its business model. For example, segments may have classifications such as healthcare and the Internet of Things (IoT).

[0029] The third level, Tech Segment, represents a classification of technologies / services within a technology area of ​​focus. For example, a Tech Segment may have a classification such as biometric monitoring.

[0030] The detailed segment, which is the lowest level of the hierarchy, represents a classification of an element technology in a technology unit or a specific service unit. For example, the detailed segment may have a classification such as disease monitoring.

[0031] With reference to FIG. 4, the operation of the analysis support server 100 for classifying the companies represented by the company records 301 of the company DB server and generating the classification records 300 will be described. Each step in FIG. 4 may be performed by the processor 201 of the computer 200 operating as the analysis support server 100 executing a program read into the memory 202. In this case, the processor 201 is the processing entity that executes the operation of each step. Alternatively, some or all of the steps of the method in FIG. 4 may be executed by a dedicated circuit such as an application specific integrated circuit (ASIC). In this case, the dedicated circuit is the processing entity that executes the operation of each step. The same applies to the operation in FIG. 5 below. The operation in FIG. 4 may be started in response to an instruction from an administrator of the analysis support server 100. It is assumed that a plurality of company records 301 are stored in the company DB server 101 at the start of the operation in FIG. 4.

[0032] In S401, the analysis support server 100 reads out a plurality of company records 301 from the company DB server 101 and stores them in the storage device 206 for subsequent processing. The analysis support server 100 may read out all company records 301 managed by the company DB server 101, or may read out only a portion of the company records 301 (for example, company records 301 related to the healthcare and medical fields).

[0033] In S402, the analysis support server 100 generates a classification record 300 for each of the multiple companies whose company records 301 have been read, and stores the classification record 300 in the storage device 206. The analysis support server 100 sets the company name, legal entity name, or other identification information of the individual company record 301 as the company record ID of the individual classification record 300. At this point, other items (e.g., sector, etc.) of the classification record 300 are blank.

[0034] In S403, the analysis support server 100 sets a sector for each of the multiple companies for which the classification record 300 has been generated, and stores this sector in the sector item of the classification record 300. The analysis support server 100 may set the sector for each company according to rules specified by an administrator of the analysis support server 100.

[0035] In S404, the analysis support server 100 sets a segment for each of the multiple companies in each sector, and stores the segment in the segment item of the classification record 300. In order to set the segments, the analysis support server 100 may divide the multiple companies in each sector into multiple groups by using natural language processing by machine learning (hereinafter simply referred to as "natural language processing").

[0036] An example of a method for dividing a plurality of companies in each sector into a plurality of groups using natural language processing will be described. For example, the analysis support server 100 may divide a plurality of companies in each sector into a plurality of groups by performing text-based natural language processing. For example, the analysis support server 100 may perform natural language processing using text (e.g., descriptions) included in the company records 301 of the plurality of companies in each sector. Such text-based natural language processing may be performed using existing technology, for example, using top2vec. With top2vec, the context of each paragraph included in the text and the connections between the paragraphs can be interpreted.

[0037] An example of text-based natural language processing will be described below. First, the analysis support server 100 extracts descriptions from the company records 301 of multiple companies included in one sector, and generates a vector representing the description of each company record 301. This vector may be, for example, a 300-dimensional vector. Then, the analysis support server 100 converts each vector into a low-dimensional (for example, 5-dimensional) vector. Finally, the analysis support server 100 divides the multiple companies included in one sector into multiple groups by clustering the low-dimensional vectors representing the multiple companies. For example, the analysis support server 100 may score the similarity of the context and connections of the paragraphs included in the description of each company record 301, and classify the descriptions into the same group if the scores are higher than a predetermined threshold. The analysis support server 100 performs the above-mentioned process for each of the multiple sectors.

[0038] After that, the analysis support server 100 sets a segment name for each group. The segment name may be determined by the analysis support server 100 according to a predetermined rule, or may be set by an administrator of the analysis support server 100.

[0039] For some sectors (for example, sectors with 500 or fewer companies), the analysis support server 100 may set segments according to rules specified by an administrator of the analysis support server 100, instead of performing natural language processing as described above.

[0040] In S405, the analysis support server 100 sets a tech segment for each of the multiple companies in each segment, and stores the tech segment in the tech segment item of the classification record 300. In order to set the tech segments, the analysis support server 100 may divide the multiple companies in each segment into multiple groups by using natural language processing. The division in S405 may use top2vec, as in the division in S404.

[0041] In S406, the analysis support server 100 sets detailed segments for each of the multiple companies in each tech segment, and stores the detailed segments in the detailed segment items of the classification record 300. For example, the analysis support server 100 may perform natural language processing using words included in the company records 301 of the multiple companies in each tech segment. The words used in the natural language processing may be words extracted from the descriptions of the company records 301, or may be keywords in the company records 301. Such word-based natural language processing may be performed using existing technology, for example, using word2vec.

[0042] An example of word-based natural language processing will be described below. First, the analysis support server 100 extracts descriptions from the company records 301 of multiple companies included in one tech segment. The analysis support server 100 generates a model (word2vec model) by performing machine learning on the words included in the descriptions. This learning may be learning using only data acquired from the corporate DB server 101, or additional learning for a model that has already been trained using a general-purpose corpus.

[0043] The analysis support server 100 then obtains a vector by inputting the keywords included in the company record 301 into the model. This vector may be, for example, a 300-dimensional vector. If the company record 301 includes multiple keywords, the analysis support server 100 may generate one vector based on the vectors of each of these keywords (for example, by extracting the maximum value or averaging).

[0044] Then, the analysis support server 100 converts each vector into a low-dimensional (e.g., five-dimensional) vector. Finally, the analysis support server 100 divides the multiple companies included in one tech segment into multiple groups by clustering the low-dimensional vectors representing the multiple companies. This clustering may be performed by, for example, the k-means method. The number of clusters may be set by the administrator of the analysis support server 100. The analysis support server 100 performs the above-mentioned process for each of the multiple tech segments.

[0045] After that, the analysis support server 100 sets a detailed segment name for each cluster. The detailed segment name may be determined by the analysis support server 100 according to a predetermined rule, or may be set by an administrator of the analysis support server 100.

[0046] As described above, the analysis support server 100 classifies segments and tech segments and detailed segments using different methods. Specifically, the upper level segments and tech segments are classified using a sentence-based method, and the lower level detailed segments are classified using a word-based method. The lower the level, the fewer the number of companies to be classified. Through experiments, the inventors found that when there are many objects to be classified, the sentence-based method can classify more accurately, and when there are few objects to be classified, the word-based method can classify more accurately. Therefore, by performing classification using different methods as described above, accurate classification can be achieved.

[0047] An example of an operation of the analysis support server 100 for providing information on companies that satisfy a specific condition will be described with reference to Fig. 5. The operation of Fig. 5 may be started in response to an instruction from a user of the analysis support server 100. It is assumed that the items of Fig. 3 are stored in the DB servers 101 to 105 and the analysis support server 100 at the start of the operation of Fig. 5.

[0048] In S501, the analysis support server 100 generates a screen for acquiring search conditions and presents it to the user. For example, when the user uses the analysis support server 100 by using the client terminal 106, the analysis support server 100 may transmit such a screen to the client terminal 106, and the client terminal 106 may display this screen on its own output device 204. Alternatively, when the user uses the analysis support server 100 directly, the analysis support server 100 may display such a screen on its own output device 204.

[0049] An example of a screen 600 for acquiring search conditions will be described with reference to Fig. 6. The screen 600 may include, for example, a pull-down list 601 for acquiring a designation of a database to be searched, a text box 602 for acquiring a search keyword, a button 603 for acquiring an instruction to execute a search, and a display area 604 for displaying search results. At the time of S601 (i.e., before execution of a search), nothing may be displayed in the display area 604.

[0050] In S502, the analysis support server 100 acquires the designation of the database to be searched and the search keyword from the user through the pull-down list 601 and the text box 602 on the screen 600. Instead of the pull-down list 601, another graphical object for accepting the selection of one item from a plurality of items, for example, a list with check boxes, may be used. The database to be searched may be selectable from five options, for example, "company information," "investor information," "literature information," "intellectual property information," and "clinical trial information." The search keyword may be any word. For example, the search keyword may be a company name or a disease name.

[0051] In S503, the analysis support server 100 executes a search using the designation of the database to be searched and the search keyword. For example, assume that "company information" is designated as the database to be searched. In this case, the analysis support server 100 may identify a company record 301 that includes the search keyword in any of the company name, legal entity name, description, and keyword, among the multiple company records 301 managed by the company DB server 101. Alternatively, the analysis support server 100 may identify a company record 301 that includes the search keyword in other items. After that, the analysis support server 100 identifies, as a matching record, a record related to the company indicated by the company name of the identified company record 301, among the multiple classification records 300 managed by the analysis support server 100.

[0052] Assume that "Investor Information" is specified as the database to be searched. In this case, the analysis support server 100 may identify an investor record 302 in which the search keyword is included in either the investor name or the keyword, among the multiple investor records 302 managed by the investor DB server 102. Alternatively, the analysis support server 100 may identify an investor record 302 in which the search keyword is included in another item. Thereafter, the analysis support server 100 identifies, among the multiple classification records 300 managed by the analysis support server 100, a record related to a company indicated by the investor name of the identified investor record 302 as a matching record. Instead of or in addition to the investor name, another item representing a company may be used among the items included in the investor record 302.

[0053] Assume that "literature information" is specified as the database to be searched. In this case, the analysis support server 100 may identify a literature record 303 in which the search keyword is included in any of the journal name, abstract, and keywords, among the multiple literature records 303 managed by the literature DB server 103. Alternatively, the analysis support server 100 may identify a literature record 303 in which the search keyword is included in other items. After that, the analysis support server 100 identifies, as a matching record, a record related to a company indicated by the affiliation name of the identified literature record 303, among the multiple classification records 300 managed by the analysis support server 100. Instead of or in addition to the affiliation name, another item representing a company may be used among the items included in the literature record 303.

[0054] Assume that "intellectual property information" is specified as the database to be searched. In this case, the analysis support server 100 may identify, among the multiple intellectual property records 304 managed by the intellectual property DB server 104, intellectual property records 304 in which the search keyword is included in any of the name, abstract, claims, and keywords. Alternatively, the analysis support server 100 may identify intellectual property records 304 in which the search keyword is included in other items. Thereafter, the analysis support server 100 identifies, among the multiple classification records 300 managed by the analysis support server 100, a record related to a company indicated by the applicant name or right holder name of the identified intellectual property record 304 as a matching record. Instead of or in addition to the applicant name or right holder name, other items representing a company may be used among the items included in the intellectual property record 304.

[0055] Assume that "clinical trial information" is specified as the database to be searched. In this case, the analysis support server 100 may identify a clinical trial record 305 in which the search keyword is included in any of the sponsor name, collaborator name, and keyword among the multiple clinical trial records 305 managed by the clinical trial DB server 105. Alternatively, the analysis support server 100 may identify a clinical trial record 305 in which the search keyword is included in other items. Then, the analysis support server 100 identifies, as a matching record, a record related to a company indicated by the implementer name, sponsor name, or collaborator name of the identified clinical trial record 305 among the multiple classification records 300 managed by the analysis support server 100. Instead of or in addition to the implementer name, sponsor name, or collaborator name, other items representing companies among the items included in the clinical trial record 305 may be used.

[0056] In S504, the analysis support server 100 displays the search results in a display area 604 of the screen 600. For example, as shown in FIG. 6, the analysis support server 100 includes in the display area 604 a message 605 showing an overview of the search results and a table 606 of hit records. Each record in the table 606 represents a matching record identified in S503. The table 606 includes a number of columns 607-612. The column 607 indicates the database specified in the pull-down list 601. The column 608 indicates the sector of the identified company. The column 609 represents a segment of the identified company. The column 610 indicates the tech segment of the identified company. The column 611 represents a detailed segment of the identified company. The column 612 represents the company name of the identified company.

[0057] Each field of columns 610, 611, and 612 may be a link to a detailed page. In response to a user selecting these links, the analysis support server 100 may present the user with a page showing details of the tech segment, the detailed segment, or the company. Records of table 606 may be sortable using each of columns 608 to 612 as a key. Records of table 606 may be filterable for each of columns 609 to 611.

[0058] As described above, according to the screen 600 of FIG. 6, records containing the search keyword are extracted from multiple data sources, and a list of companies associated with the records is displayed. In addition, the sectors, segments, tech segments, and detailed segments are displayed together with the company names of companies that satisfy the search conditions. Furthermore, data sources that store records containing the search keyword are displayed together with the company names of companies that satisfy the search conditions. Therefore, the user can accurately grasp the trends of companies that satisfy the specified search conditions.

[0059] The invention is not limited to the above-described embodiment, and various modifications and variations are possible within the scope of the gist of the invention. [Explanation of symbols]

[0060] 100 analysis support server, 101 corporate DB server, 106 client terminal

Claims

1. An analysis support server for providing information for supporting analysis of a company, An acquisition means for acquiring a search keyword specification from a user; an identification means for identifying one or more records including the search keyword from a plurality of records read from a plurality of data sources, and identifying one or more companies from the one or more records; A classification means for classifying a plurality of companies into each of a plurality of hierarchies including a first hierarchical level and a second hierarchical level lower than the first hierarchical level; a display means for displaying a list showing the one or more identified companies; The classification means classifies companies in the first layer using sentence-based natural language processing, and classifies companies in the second layer using word-based natural language processing; The display means displays, for each of the plurality of hierarchical levels, classification items assigned to a company shown in the list in association with the company.

2. 2. The analysis support server according to claim 1, wherein said display means displays a data source from which a record used to identify a company shown in said list was read, in association with said company.

3. A program for causing a computer to function as each of the means of the analysis support server according to claim 1 or 2.

4. 1. A computer-implemented method for providing information to support an analysis of a company, the method comprising: An acquisition step of acquiring a search keyword specification from a user; an identifying step of identifying one or more records including the search keyword from a plurality of records read from a plurality of data sources, and identifying one or more companies from the one or more records; A classification step of classifying a plurality of companies into each of a plurality of hierarchies including a first hierarchical level and a second hierarchical level lower than the first hierarchical level; and displaying a list showing the one or more identified companies, The classification step includes classifying the companies in the first layer using sentence-based natural language processing and classifying the companies in the second layer using word-based natural language processing; The analysis support method, wherein the display step includes displaying, for each of the plurality of hierarchical levels, classification items assigned to a company shown in the list in association with the company.

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