Compatibility Evaluation Device, Compatibility Evaluation Method, and Program

Abstract

This compatibility evaluation device for evaluating the compatibility between a subject and an evaluation target is provided with: an input unit configured to input subject text information, which is text information about the subject; an evaluation unit configured to calculate a subject feature quantity that is a non-financial feature quantity from the subject text information, and also calculate an evaluation target feature quantity that is a non-financial feature quantity from evaluation target text information, which is text information about the evaluation target; and a compatibility calculation unit configured to calculate said compatibility on the basis of the subject feature quantity and the evaluation target feature quantity.

Description

Technical Field

[0001] The present invention relates to a technique for evaluating the compatibility between a target person (such as a job seeker) and an evaluation target (such as a company) by using non-financial information of the evaluation target (company, etc.).

Background Art

[0002] Non-financial information of companies represented by ESG (Environment, Society, Governance) has been emphasized in evaluating the social value of companies.

[0003] Hitherto, in the judgment of corporate value in investment and the like, the economic value of the company, that is, financial information (such as profit amount and profit rate), has been used as the main evaluation material. In recent years, however, not only financial information but also non-financial information such as efforts towards ESG and SDGs has become an important material for selecting investment destinations after evaluating the social value of companies. On the other hand, as for the way of the company itself, there has also emerged a corporate activity that emphasizes social value rather than economic value, such as the corporate activity of Grameen Bank.

[0004] On the other hand, in recent years, due to the influence of the coronavirus and the like, what job seekers and job changers seek from companies is also changing. There is a tendency to place emphasis on non-financial information, such as working in a field where one can find meaning and interest, rather than on the economic aspects such as annual income and treatment in the past. This is a tendency confirmed by actual questionnaires and the like.

[0005] However, the methods for evaluating companies and evaluating compatibility with companies using these non-financial information have not been established, and there is a problem that job seekers cannot efficiently select companies.

Prior Art Documents

Non-Patent Documents

[0006]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] When evaluating a company's social value, currently, generally an index of ESG investment brands (referred to as the ESG index) is used. However, the ESG index is evaluated based on a company's public information (updated once a year, etc.), and the details of the evaluation criteria and process are not publicly available.

[0008] Also, it is assumed that there are many cases where a company's public information is created with the involvement of external consulting and is information based on subjective evaluations. Therefore, it is unclear whether the ESG efforts (for example, efforts related to the SDGs) in a company's public information are in line with the actual situation of the company's activities. The public information of a company and the evaluations by rating agencies are also subjective and a black box. In addition, in conventional evaluation methods, there may be biases by companies, so there is a possibility that the evaluation does not conform to the actual situation of the company's activities.

[0009] From the above points, when a job seeker selects an employment destination company that is a good fit for themselves, public information such as the ESG index is insufficient. Therefore, it is necessary to investigate each company's information, which requires a great deal of effort. A method for automatically evaluating the compatibility between a job seeker and a company is desired.

[0010] The present invention has been made in view of the above points, and an object thereof is to provide a technology that enables automatic evaluation of the compatibility between a target person (for example, a job seeker) and an evaluation target (for example, a company).

Means for Solving the Problems

[0011] According to the disclosed technology, there is provided a compatibility evaluation device for evaluating the compatibility between a target person and an evaluation target, an input unit configured to input target person text information, which is text information of the target person, the target person text information, Based on the relevance to the social value feature amount generated from text information related to the evaluation of social value calculate a target person feature quantity, and evaluation target text information, which is text information of the evaluation target and based on the relevance to the social value feature amount an evaluation unit configured to calculate an evaluation target feature quantity, a compatibility calculation unit configured to calculate the compatibility based on the target person feature quantity and the evaluation target feature quantity A compatibility evaluation device including these components is provided.

Effect of the Invention

[0012] According to the disclosed technology, a technology is provided that enables automatic evaluation of the compatibility between a target person (for example, a job seeker) and an evaluation target (for example, a company).

Brief Description of the Drawings

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Mode for Carrying Out the Invention

[0014] Hereinafter, embodiments (these embodiments) of the present invention will be described with reference to the drawings. The embodiments described below are merely examples, and the embodiments to which the present invention is applied are not limited to the following embodiments.

[0015] For example, in the following embodiments, a company is assumed as an evaluation target for compatibility with job seekers, but the technology according to the present invention is applicable not only when the evaluation target is a company. Also, the entity (referred to as the target person) that evaluates compatibility with the evaluation target is not limited to job seekers.

[0016] For example, it is also possible to evaluate the compatibility between a certain target person and an individual, a group, a country, a local government, etc. by the technology according to the present invention. Also, in the following embodiments, processing is performed on Japanese text, but this is an example, and the technology according to the present invention is applicable to any language.

[0017] (Problem, Outline of Embodiment) As described above, in the prior art, an ESG index is often used when evaluating the social value of a company. However, it is uncertain whether the ESG index reflects the actual state of a company's activities that change every moment. First, regarding this point, an example of the conventional ESG rating method will be described.

[0018] For example, as disclosed in "MSCI (2017) MSCI ESG Research ESG Rating Methodology Summary", in the conventional ESG rating, first, key issues are selected and weighted in each industry, and for each key issue, risk exposure and risk management are scored for each company. A key issue score is obtained from these two scores.

[0019] Then, to conduct a relative evaluation of companies in the industry, the weighted average score of each company is standardized to determine the Industry Adjusted Score, and based on this, the original ESG score is determined.

[0020] In determining the ESG score as described above, public information of companies is used as the information source, and annual reports such as sustainability reports are used as public information.

[0021] However, it is not possible to fully evaluate non-financial elements that change moment by moment, such as the efforts towards the SDGs, based on the public information of companies that is only released once a year. In addition, in many cases, external consulting and the like are involved in the creation of the public information of companies, and it is unclear whether the actual situation is accurately reflected.

[0022] From the above points, when job seekers select a company they are compatible with, public information such as ESG indices is insufficient. Therefore, it is necessary to investigate the information of each company, which requires a great deal of effort. A method for automatically evaluating the compatibility between job seekers and companies is desired.

[0023] To solve the above problems, in this embodiment, it is possible to automatically evaluate the non-financial compatibility with companies from text information such as the motivation of job seekers.

[0024] That is, the compatibility evaluation device 100 in this embodiment solves the above problems and enables job seekers to efficiently evaluate the non-financial compatibility with companies without investigating the information of each company. In this embodiment, the non-financial compatibility is calculated from the perspective of social value.

[0025] For the purpose of comparison with the non - financial compatibility evaluation method according to this embodiment, a non - financial compatibility evaluation method based on the evaluation of enterprises by the prior art will be described with reference to FIG. 1. As shown in FIG. 1, in the prior art, a rating company uses publicly available information, for example, information published once a year, to conduct an evaluation in a process with unclear details and then publishes the evaluation results. The evaluation results are, for example, rating information such as "A". Even if a job - seeking student sees such information, he / she cannot grasp the actual situation of the enterprise. Therefore, in order to judge the compatibility with the enterprise, it is necessary to collect more information about the enterprise, which requires a great deal of effort.

[0026] FIG. 2 is a diagram showing an overview of the operation of the compatibility evaluation apparatus 100 in this embodiment. As shown in FIG. 2, the feature generation module 130 calculates and stores feature quantities related to social values from text information (e.g., target sentences of SDGs) related to the evaluation of social values read from the text DB 132. Note that the feature generation module 130 may also be referred to as a feature generation unit.

[0027] Subsequently, the evaluation module 120 inputs text information including a plurality of sentences such as news, press releases, and SNS obtained from the text DB 150, evaluates the relevance between the text information and the feature quantities generated by the feature generation module 130, and outputs the feature quantities (evaluation target feature quantities) of the enterprise based on the evaluation results. In addition, the evaluation module 120 inputs text information (such as motivation for applying) described by the job - seeking student, evaluates the relevance between the text information and the feature quantities generated by the feature generation module 130, and outputs the feature quantities (target person feature quantities) of the job - seeking student based on the evaluation results.

[0028] Then, the correlation calculation unit 140 calculates the compatibility between the enterprise and the job - seeking student by calculating the relevance (similarity, etc.) between the evaluation target feature quantity and the target person feature quantity. The compatibility is calculated, for example, as similarity.

[0029] Hereinafter, as an example, the configuration and operation examples of the compatibility evaluation apparatus 100 will be described in more detail.

[0030] (Configuration Example of Compatibility Evaluation Device 100) First, a configuration example of the compatibility evaluation device 100 will be described. FIG. 3 shows a detailed configuration example of the compatibility evaluation device 100. As shown in FIG. 3, the compatibility evaluation device 100 includes an input unit 110, an evaluation module 120, a feature generation module 130, a compatibility calculation unit 140, a text DB 150, and an output unit 160.

[0031] The evaluation module 120 includes a text analysis unit 121 and an evaluation unit 122. The feature generation module 130 includes a feature storage unit 131, a text DB 132, and a feature calculation unit 133.

[0032] The general operation outlines of the evaluation module 120, the feature generation module 130, and the compatibility calculation unit 140 are as described with reference to FIG. 2. The detailed operations of each part constituting the compatibility evaluation device 100 will be described in the operation example section below.

[0033] <Hardware Configuration Example> The compatibility evaluation device 100 can be realized, for example, by causing a computer to execute a program. This computer may be a physical computer or a virtual machine on the cloud.

[0034] That is, the compatibility evaluation device 100 can be realized by executing a program corresponding to the processing performed by the compatibility evaluation device 100 using hardware resources such as a CPU and a memory built into the computer. The above program can be recorded on a computer-readable recording medium (such as a portable memory), saved, distributed, or provided through a network such as the Internet or email.

[0035] Figure 4 is a diagram showing an example of the hardware configuration of the above computer. The computer in Figure 4 includes a drive device 1000, an auxiliary storage device 1002, a memory device 1003, a CPU 1004, an interface device 1005, a display device 1006, an input device 1007, an output device 1008, etc., which are mutually connected by a bus BS.

[0036] A program for realizing the processing on the computer is provided by a recording medium 1001 such as a CD-ROM or a memory card, for example. When the recording medium 1001 storing the program is set in the drive device 1000, the program is installed from the recording medium 1001 via the drive device 1000 into the auxiliary storage device 1002. However, the installation of the program does not necessarily have to be performed from the recording medium 1001, and it may be downloaded from another computer via a network. The auxiliary storage device 1002 stores the installed program and also stores necessary files, data, etc.

[0037] When an instruction to start the program is given, the memory device 1003 reads out and stores the program from the auxiliary storage device 1002. The CPU 1004 realizes the functions related to the compatibility evaluation device 100 according to the program stored in the memory device 1003. The interface device 1005 is used as an interface for connecting to a network and functions as a transmission unit and a reception unit. The display device 1006 displays a GUI (Graphical User Interface) etc. by the program. The input device 1007 is composed of a keyboard, a mouse, buttons, or a touch panel, etc., and is used to input various operation instructions. The output device 1008 outputs the calculation result.

[0038] (Operation example of the compatibility evaluation device 100) Next, an operation example of the compatibility evaluation device 100 will be described. The compatibility evaluation device 100 can evaluate the compatibility with job seekers for various companies. Hereinafter, an example of performing a compatibility evaluation between a certain job seeker and a certain company (referred to as the "company to be evaluated for compatibility") will be described.

[0039] In this embodiment, first, feature amounts are generated from text information related to non-financial features and stored. This stage is called the feature amount generation phase. Next, using the stored feature amounts, a compatibility evaluation between the job seeker and the target company for compatibility evaluation is performed. This stage is called the evaluation phase. Hereinafter, each of the feature amount generation phase and the evaluation phase will be described.

[0040] (Feature amount generation phase) In the text DB 132 in the feature generation module 130, for example, a plurality of sentences (text information) representing goals regarding activities that enhance social value are stored. Specifically, for example, 169 target sentences of the SDGs are stored. The targets and goals may be referred to as indicators of social value.

[0041] Note that using the target sentences of the SDGs as information used to extract non-financial features in the feature generation module 130 is just an example. This is an example when focusing on the social value in a company, and based on this, the compatibility between the company and the job seeker can be evaluated from the aspect of social value. When extracting non-financial features from other viewpoints, sentences other than the target sentences of the SDGs are used.

[0042] The feature calculation unit 133 inputs a plurality of sentences read from the text DB 132 and performs morphological analysis on each sentence. By morphological analysis, keywords and the like can be obtained from the input sentences. Any technique can be used for morphological analysis. For example, natural language processing techniques such as the Tf-idf method, co-occurrence degree analysis, and dependency analysis, as well as text mining techniques, can be utilized. Also, morphological analysis tools such as Mecab, JUMAN, and ChaSen may be used.

[0043] Note that instead of extracting keywords from the text information as described above, arbitrary keywords may be set subjectively (manually).

[0044] In this embodiment, 109 feature quantities are generated in the feature calculation unit 133 from the 169 target sentences of the SDGs. The feature calculation unit 133 generates a feature quantity composed of vectors from one or more keywords using pre-trained word embedding vectors such as word2Vec, GloVe, and fastText. At that time, averaging, normalization, etc. between multiple keywords may be appropriately performed.

[0045] Fig. 5 shows an image of generating a feature quantity vector using word2Vec. In the example shown in Fig. 5, from one target consisting of three keywords "safety", "security", and "health", a feature quantity (a vector such as 200 dimensions) is obtained by word2Vec.

[0046] The feature quantity obtained by the feature calculation unit 133 is stored in the feature storage unit 131. For example, in the feature storage 131, feature quantities (vectors) are stored for each of the 109 targets. Along with the feature quantity, the target sentence or multiple keywords based on which the feature quantity is calculated may be stored in the feature storage 131.

[0047] (Evaluation phase) Next, an operation example of the compatibility evaluation device 100 in the evaluation phase will be described according to the procedure shown in the flowchart of Fig. 6. Note that the order of the processes shown in Fig. 6 is an example, and the processes may be performed in any order as long as the evaluation result can be calculated. Also, multiple processes may be executed in parallel.

[0048] As a premise of the flowchart in Fig. 6, it is assumed that the text DB 150 stores text information about the company to be evaluated for compatibility. The text information is information obtained in real time about the company to be evaluated for compatibility, and the text DB 150 is updated at any time with the real-time text information.

[0049] The text information may be any text information regarding the target company for compatibility evaluation, such as press releases, news releases, SNS, etc. In this embodiment, it is assumed that the news release of the target company provided by a PR company is input into the text DB 150. Note that the text DB 150 may be provided outside the compatibility evaluation device 100.

[0050] <s101> In S101 (Step 101), text information such as the applicant's motivation described by the job seeker is input into the input unit 110. For example, a web page is displayed on the job seeker's terminal from the input unit 110, and the job seeker inputs the text information on the web page. The input text information is passed to the text analysis unit 121 of the evaluation module 120.

[0051] Also, in S101, text information (such as news releases) about the company to be evaluated for compatibility is input from the text DB 150 into the evaluation module 120.

[0052] Examples of the sentences of the applicant's motivation input by the job seeker include the following sentences.

[0053] ".... I learned that your company provides web services for small and medium-sized enterprises and can engage in sales individually or in a small team, so I am interested. I want to work in an environment where my value can be easily seen while making use of my knowledge of IT infrastructure, so I have applied to your company."

[0054] <s102> In S102, the text analysis unit 121 in the evaluation module 120 performs text analysis on the text information (which may be referred to as "article" or "document") input in S101.

[0055] Here, text analysis is performed on each of the text information of the job hunting motivation, etc. by job hunting students (referred to as "job hunting student text information") and the text information of the target company for compatibility evaluation such as news releases (referred to as "company text information").

[0056] Specifically, for example, in the same way as the method described in the "feature quantity generation phase", the text analysis unit 121 performs morphological analysis of the input text and generates feature quantities for one or more keywords obtained by morphological analysis using word embedding vectors or the like.

[0057] For the sake of convenience of explanation, the feature quantity obtained from the job hunting student text information is called the job hunting student feature quantity, and the feature quantity obtained from the company text information is called the company feature quantity.

[0058] <s103> In S103, the evaluation unit 122 calculates the relevance (specifically, similarity) between the feature amount obtained by the text analysis unit 121 and each feature amount (each target) read from the feature storage unit 131. Here, the evaluation unit 122 calculates the similarity between the job-seeker feature amount and each feature amount read from the feature storage unit 131, and the similarity between the company feature amount and each feature amount read from the feature storage unit 131.

[0059] For example, assuming that 109 feature amounts (vectors) corresponding to 109 targets are stored in the feature storage unit 131, the evaluation unit 122 calculates the similarity between each of the 109 feature amounts and the feature amount obtained by the text analysis unit 121.

[0060] In calculating the similarity, any method can be used as long as it can calculate the similarity between two pieces of information. For example, cosine similarity can be used. When using cosine similarity, the similarity between feature amount x and feature amount y can be calculated by the following formula.

[0061] cos(x,y)=x·y / |x|×|y| For example, the evaluation unit 122 can extract any number of keywords with particularly high similarity from the input text (news release if it is company text information) for each feature amount (that is, for each target) stored in the feature storage unit 131, and use the average of the similarities of that number, the normalized value, etc. as the similarity of the calculation result.

[0062] For example, the evaluation unit 122 may extract 10 keywords with high similarity. For example, for a specific target A, assume that keyword 1, keyword 2,..., keyword 10 are the top 10 keywords with high similarity according to the above similarity calculation.

[0063] In this case, the feature amounts of each of the 10 keywords are defined as feature amount 1, feature amount 2,...., feature amount 9, and feature amount 10. Assuming that the feature amount corresponding to target A stored in the feature memory unit 131 is feature amount A, the evaluation unit 122 calculates the similarity 1 between feature amount 1 and feature amount A, the similarity 2 between feature amount 2 and feature amount A,...., the similarity 9 between feature amount 9 and feature amount A, and the similarity 10 between feature amount 10 and feature amount A.

[0064] For example, the evaluation unit 122 can calculate the average value, the minimum value, and the maximum value of the 10 similarities 1 to 10 with respect to target A, and output these as the calculation results of the similarities regarding target A. Note that such a calculation method is just an example.

[0065] The above similarity calculation is executed for each of the job hunting student text information and the company text information.

[0066] <s104> In S104, the compatibility calculation unit 140 calculates the compatibility between the job seeker and the company to be compatibility-evaluated based on the calculation result obtained by the evaluation unit 122. The method for calculating the compatibility is not limited to a specific method, but for example, there are the following methods.

[0067] For example, assume that the target with the highest similarity in the job seeker's text information is Target A, and the target with the highest similarity in the company's text information is Target B.

[0068] In this case, for example, the compatibility calculation unit 140 extracts keywords with high similarity to the feature amount of Target A (for example, the top N (N is an integer of 1 or more) in terms of similarity) from the job seeker's text information, calculates the feature amount of the extracted keywords (referred to as the target feature amount), extracts keywords with high similarity to the feature amount of Target B (for example, the top N in terms of similarity) from the company's text information, and calculates the feature amount of the extracted keywords (referred to as the evaluation target feature amount). Note that the calculation of these feature amounts may also be performed by the evaluation module 120.

[0069] Then, the compatibility calculation unit 140 calculates the similarity that represents the compatibility between the job seeker and the company to be compatibility-evaluated, for example, by calculating the cosine similarity between the target feature amount and the evaluation target feature amount. If the cosine similarity is high, it can be determined that the compatibility is good.

[0070] In addition to calculating the compatibility using the feature amounts of the keywords extracted from the job seeker's text information / company's text information as described above, the compatibility may also be calculated using the feature amounts of the keywords extracted from the target text.

[0071] An example in this case will be described. For example, assume that the target with the highest similarity to the job-seeking student text information is Target A, and the target with the highest similarity to the company text information is Target B. In this case, the compatibility calculation unit 140 calculates the feature amount of the keyword extracted from the target sentence of Target A (referred to as the target person feature amount), and also calculates the feature amount of the keyword extracted from the target sentence of Target B (referred to as the evaluation target feature amount). Then, the compatibility calculation unit 140 calculates the compatibility between the job-seeking ability and the company to be evaluated for compatibility, for example, by calculating the cosine similarity between the target person feature amount and the evaluation target feature amount. If the cosine similarity is high, it can be determined that the compatibility is good.

[0072] Also, for example, when the target with the highest similarity to the job-seeking student text information is Target A, and the target with the highest similarity to the company text information is also Target A, and assume that the keyword for Target A is "Establishment and growth support of small and medium-sized enterprises." In this case, the feature amount of the job-seeking student (the feature amount of "Establishment and growth support of small and medium-sized enterprises") and the feature amount of the company (the feature amount of "Establishment and growth support of small and medium-sized enterprises") are the same, a high similarity is obtained, and it can be determined that the compatibility between the company and the job-seeking student is good.

[0073] <s105> The output unit 160 outputs the evaluation result. The output of the evaluation result may be, for example, to graphically display it on the screen of the UI (user interface) of the display of the compatibility evaluation device 100, or to output a list of numerical values. It may also be displayed as a web page on the terminal of the user (job seeker).

[0074] Regarding the information to be output, not only the result of the compatibility calculation (the similarity described above), but also the similarity between the job seeker text information and each target, and the similarity between the company text information and each target may be output.

[0075] With reference to FIGS. 7 and 8, a display example when outputting the similarity between the job seeker text information and each target will be described.

[0076] FIG. 7 shows an example in which nine target sentences are displayed for a certain goal (for example, goal 8). As shown in FIG. 7, the similarity such as 0.589 is displayed for each target. An image as shown in FIG. 7 may be displayed for each of all the goals.

[0077] Also, when the user selects (clicks) a certain target on the screen, for example, the screen shown in FIG. 8 can be displayed. FIG. 8 is a screen showing the details of the selected target. Also, the number of keywords used in the calculation can be specified on this screen. As described above, in the previous example, the number of keywords = 10.

[0078] Regarding the similarity between the company text information and each target, screens such as FIGS. 9 and 10 can be displayed. For the convenience of illustration, a part of the display screen is shown. In this example, 109 targets are grouped into 17 SDGs goals, the similarity for each target for each goal (multiple similarities corresponding to multiple keywords) is calculated, and the ratio is displayed.

[0079] In the example of FIG. 9, for each target number shown on the horizontal axis, the ratio of values for each range of similarity is displayed. In FIG. 9, the ranges of the ratio are distinguished by hatching, but they may also be distinguished by differences in color. Further, in the example of FIG. 9, the goal numbers corresponding to a plurality of target numbers are shown on the horizontal axis. In the example of FIG. 10, the annual change in the evaluation results is shown. In relation to FIGS. 9 and 10, information about the corporate text information of the evaluation target may be displayed. The said information is, for example, the target company name, the period and number of news releases, etc.

[0080] For example, a user (job seeker) can comprehensively judge the compatibility from the information showing the relationship between the job seeker text information and the target shown in FIGS. 7 and 8, and the information showing the relationship between the corporate text information and the target shown in FIGS. 9 and 10.

[0081] For example, if the similarity between the job seeker text information and Target A is high and the similarity between the corporate text information and Target A is also high, the user can judge that the compatibility with the target company for compatibility evaluation is good in terms of the social value of Target A. Also, if the similarity between the job seeker text information and Target B is high and the similarity between the corporate text information and Target B is low, the user can judge that the compatibility with the target company for compatibility evaluation is not good in terms of the social value of Target B.

[0082] (Effect of the Embodiment) As described above, with the compatibility evaluation device 100 according to the present embodiment, from text information distributed daily such as press, news releases, and SNS, regardless of public information such as a company's annual report, the non-financial efforts of the company for various evaluation axes (for example, each target) can be objectively evaluated. Further, since the evaluation is performed by inputting the text data of the company, an objective and real-time evaluation based on the actual state of the company's activities is possible.

[0083] Since the compatibility evaluation device 100 calculates the compatibility from the non-financial characteristics of the above-mentioned company and the characteristics obtained from the text information of the job seeker's motivation for applying, it is possible to automatically calculate the compatibility based on an objective evaluation.

[0084] Also, if there is text such as the motivation of job seekers, it is possible to automatically conduct a compatibility evaluation for a large number of companies.

[0085] (Appendix) This specification discloses at least a compatibility evaluation device, a compatibility evaluation method, and a program for each of the following items. (Item 1) A compatibility evaluation device for evaluating the compatibility between a subject and an evaluation target, An input unit configured to input subject text information, which is the text information of the subject, An evaluation unit configured to calculate a subject feature amount, which is a non-financial feature amount, from the subject text information, and calculate an evaluation target feature amount, which is a non-financial feature amount, from the evaluation target text information, which is the text information of the evaluation target, A compatibility calculation unit configured to calculate the compatibility based on the subject feature amount and the evaluation target feature amount A compatibility evaluation device comprising the above. (Item 2) Further comprising a feature amount generation unit configured to generate a feature amount as a social value feature amount from text information related to the evaluation of social value, The evaluation unit calculates the subject feature amount based on the relevance between the subject text information and the social value feature amount, and calculates the evaluation target feature amount based on the relevance between the evaluation target text information and the social value feature amount The compatibility evaluation device according to Item 1. (Item 3) The subject is a job seeker, the evaluation target is a company, and the subject text information is text information describing the motivation of the subject to join the company. The compatibility evaluation device according to Item 1 or Item 2. (Item 4) A compatibility evaluation method executed by a compatibility evaluation device for evaluating the compatibility between a subject and an evaluation target, An input step of inputting subject text information, which is the text information of the subject, An evaluation step of calculating a subject feature amount, which is a non-financial feature amount, from the subject text information, and calculating an evaluation target feature amount, which is a non-financial feature amount, from the evaluation target text information, which is the text information of the evaluation target; A compatibility calculation step of calculating the compatibility based on the subject feature amount and the evaluation target feature amount; A compatibility evaluation method comprising the above. (Item 5) A program for causing a computer to function as each part in the compatibility evaluation apparatus according to any one of Items 1 to 3.

[0086] Although the present embodiment has been described above, the present invention is not limited to such a specific embodiment, and various modifications and changes are possible within the scope of the gist of the present invention described in the claims.

Explanation of Signs

[0087] 100 Compatibility evaluation apparatus 110 Input unit 120 Evaluation module 121 Text analysis unit 122 Evaluation unit 130 Feature generation module 131 Feature storage unit 132 Text DB 133 Feature calculation unit 140 Compatibility calculation unit 150 Text DB 160 Output unit 1000 Drive device 1001 Recording medium 1002 Auxiliary storage device 1003 Memory device 1004 CPU 1005 Interface device 1006 Display device 1007 Input device 1008 Output device

Claims

1. A compatibility evaluation device for evaluating the compatibility between a target person and an evaluation target, comprising: an input unit configured to input target person text information, which is text information of the target person; an evaluation unit configured to calculate a target person feature amount based on the relevance between the target person text information and a social value feature amount generated from text information related to the evaluation of social value, and calculate an evaluation target feature amount based on the relevance between the evaluation target text information, which is the text information of the evaluation target, and the social value feature amount; a compatibility calculation unit configured to calculate the compatibility based on the target person feature amount and the evaluation target feature amount A compatibility evaluation device comprising the above components.

2. The compatibility evaluation device according to claim 1, further comprising a feature amount generation unit configured to generate the social value feature amount from the text information related to the evaluation of social value. The compatibility evaluation device according to claim 1.

3. The target person is a job seeker, the evaluation target is a company, and the target person text information is text information describing the motivation of the target person to join the company. The compatibility evaluation device according to claim 1 or 2.

4. A compatibility evaluation method executed by a compatibility evaluation device for evaluating the compatibility between a target person and an evaluation target, comprising: an input step of inputting target person text information, which is text information of the target person; an evaluation step of calculating a target person feature amount based on the relevance between the target person text information and a social value feature amount generated from text information related to the evaluation of social value, and calculating an evaluation target feature amount based on the relevance between the evaluation target text information, which is the text information of the evaluation target, and the social value feature amount; a compatibility calculation step of calculating the compatibility based on the target person feature amount and the evaluation target feature amount A compatibility evaluation method comprising the above steps.

5. A program for causing a computer to function as each part in the compatibility evaluation device according to any one of claims 1 to 3.

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