A system for the individual psychological and emotional state analysis is developed. The aim is to assess the individual through practical and recommendations social networks. The assessment data, this area problems and the system's relevance analysis are studied. The diagrams are developed that describe the system logic and structure. System requirements and a prototype application description are done According to RUP methodology, which stimulates an individual analysis system activity is created. Social network, information system, intelligent system, information technology, psychological analysis, personality trait, emotional state, big data, psychological state, system analysis, information resource, text analysis, data processing, social network personal analysis, Facebook profile, psychological type, modern machine learning technology, psychological portrait, decision making COLINS-2021: 5th International Conference on Computational Linguistics and Intelligent Systems, April 22-23, 2021, Kharkiv, Ukraine ORCID: 0000-0001-9606-0267 (O. Oborska); 0000-0003-0697-7384 (V. Andrunyk); 0000-0003-4040-7588 (L. Chyrun); 0000-0001-92808098 (R. Hasko); 0000-0001-9190-7051 (A. Vysotskyi); 0000-0003-4932-4113 (S. Mushasta); 0000-0003-2304-8149 (O. Petruchenko); 0000-0003-0809-1480 (I. Shakleina)
Life poses professional and personal problems to the person constantly. It is not enough to have the skills, the mind, the gift, and the honesty to succeed. Success also depends on a person's self-esteem, attractiveness, competence, competitiveness, communication skills, ability to be a team player. In market relations, for a modern specialist, a psychological culture, the main components of knowledge of oneself, another person, a culture of behaviour and communication, is no less important than, for example, knowledge of a personal computer or knowledge of a person a foreign language. The success of affairs depends on the psychological culture of human. It is relevant in today's world in the age of social networking, where accounts have become an inexhaustible source of information about each individual. Posts on Twitter, Vkontakte, and Facebook posts, even subscriptions to certain bands and music, are valuable information that allows you to find out a nearly complete psychological portrait of a person before getting to know her. Social network users often behave differently than in real life, which is one of the problems in determining psychological and emotional state.
On the other hand, given this fact, you can understand how a person sees himself in the outside world and what points of interaction with him can found. Therefore, it becomes urgent to learn how to behave
2021 Copyright for this paper by its authors. in a social network to feel as if they are a socially successful person. It is necessary to learn how to use social networks properly and learn to filter public content because the ability to present this information psychologically correctly largely depends on the success of each person. There are few information resources and programs available to help determine a person's psychological or emotional state through social networks. Mostly, it comes down to apps or programs that offer some tests to determine your character, "compatibility" with your friends, and more. However, they all have one major flaw - they are not automated. Everyone is required to undergo tests each time, and often the results of these tests are different. A person strives to minimise their efforts to obtain the information they seek. It is more convenient for the user, by installing the application or by registering on the information resource, to be constantly acquainted with assessing their psychological or emotional state. Therefore, the construction of information systems that will perform this work is relevant.
The purpose of the work is to create a system for analysing the personal user data (messages on social networks, tweets, etc.) and building a psychological portrait of a person with the generation of conclusions and recommendations of the psychological and emotional state of the individual.
Social networks are becoming more and more popular. A considerable number of people have accounts in several of them at once. With the help of social networks, people find their friends, socialise, share interests, share information. At the same time, actively using social networks, a person informs himself of various content. Is it possible to say anything specific about the person based on this information or to use it, for example, for job evaluation? The idea of evaluating a person's personality by their activity in social networks is increasingly capturing the minds of researchers. Several such attempts have recently made. Donald Kluemper and his companions published an article [1] in 2012describing how three specially trained evaluators more or less successfully identified the personal traits of Facebook users on their profile. The researchers set out to test whether the owner of the Big Five characteristics could be determined based on an analysis of a Facebook profile, predict the likelihood of hiring that person for work, and their future effectiveness, although the predictive validation of expert estimates was low and the sample was small. Many unique features are reflected in the Facebook profile, according to the authors. For example, the number of friends may be associated with extraversion, people with a high level of awareness are more likely to be more cautious about the content of messages and comments they write online, and people with expressed kindness will be more trusting, so their profile will contain more information that is personal. To test these assumptions, three specially trained evaluators, each of whom completed two-hour training, were asked to analyse 274 profiles of real people on the Facebook network. In addition, assess based on this information is the holders of these profiles of each Big Five features (neuroticism, extraversion, openness to experience, goodwill, awareness) and determine the likelihood of hiring them for a starting position in the service industry. Six months after the evaluation procedure, the researchers attempted to contact the leaders of the evaluated study participants. They asked them to fill in questionnaires about the effectiveness of the employee and his or her "civic" behaviour toward colleagues and the organisation as a whole. Such data is collected from 56 executives.
The results showed that the estimates of personality traits obtained from the experts are significantly
related (r = 0.23-0.44) to the estimates obtained by the personal questionnaire. Thus, analysing a
Facebook profile is a good way to evaluate the personality traits of its owner. A significant correlation
is also found between the personality traits of a Facebook profile and the performance estimates
received from executives. The most predictable power is emotional stability (r = 0.27) and goodwill (r
= 0.31). Among the estimates of personality traits obtained from the most evaluated, the only
extraversion correlates with work efficiency (r = 0.28). Thus, Facebook profile analysis has proven to
be a more valid predicting performance than personal questionnaires. Considering that it took an
average of 5 to 10 minutes to analyse one profile of assessors, this method of personality assessment is
desirable to use in the selection process. However, its use involves some legal and ethical issues. For
example, during an interview, the employer may not ask questions regarding race or religion, sexual
orientation, marital status, etc. When analysing the same profile on the social network, this information
can become obvious and affect the hiring decision. However, researchers at the University of
Pennsylvania (https://www.upenn.edu/) and The Psychometrics Centre - University of Cambridge
(https://www.psychometrics.cam.ac.uk/) have done. In September 2013, they published an article [2]
describing the results of the analysis of 700 million words, phrases and topics collected from Facebook
posts by 75,000 people. The analysis showed striking differences in the frequency of different words
and phrases between people of different sex, age, and personality traits. Here, for example, the
differences between men and women are shown in Figs. 1a, the differences between age groups (
a) b) Figure 1: а) The differences between men and women and b) age differences [2]
The Psychometrics Centre - University of Cambridge (https://www.psychometrics.cam.ac.uk/) is also trying to predict the individual characteristics of a person by liking it on Facebook. An analysis of such preferences of 58,000 people showed [3] that their model assumes belonging to: White Americans or African-Americans in 95% of cases, Gender - in 93% of cases, Sexual orientation - in 88% of cases in men and 75% of subjects in women, Democrats or Republicans in 85% of cases, Christians or Muslims in 82% of cases.
The accuracy of the prediction of the remaining dichotomous variables shows in Fig. 3a.
In terms of personality traits, the predictive validity is lower: intelligence (r = 0.39), extraversion (r = 0.40), openness (r = 0.43). However, it is quite comparable to the validity of personality tests. The accuracy of the prediction of other individual features shows in Fig. 3b. These results suggest that many tasks for assessing certain specific characteristics of a person (including personality traits) can analyse their profile and activity on social networks. In doing so, it will not be a person but a computer. In recruitment, for example, every second person searches for a candidate's social networking profile before inviting him for an interview. Of course, there are difficulties and limitations. First, as soon as the majority learns that and with what accuracy their social media profiles can be determined, the majority will either cease using them actively, or create several accounts, or, using social networks, use a specific strategy by creating a certain image of yourself (such is the socially desirable use of Facebook). There are also ethical issues. However, the prospects are astounding. a) b) Figure 3: Prediction of a) individual specifics and b) other characteristics of the user's Facebook [3]
Analysis of known remedies. Psychological research is very relevant in the modern world, especially in developing information technologies and social networks. However, there are currently no software or information systems on the software market that would address this comprehensively and thoroughly. All existing applications can \ divided into the following subgroups: 1. WEB applications and online tests. This software functions as a standard test: you are asked to take a test and answer specific questions. Based on these answers, the program finds a match in its database or, using different algorithms, determines the percentage composition of the answers, and selects the most competitive one. The problem with such programs is that they are not complete information systems [4-15]. Often, this is generally a separate web page with a set of scripts. Such programs cannot learn and analyse vast amounts of information [16-20]. 2. Statistical systems. This software can handle large data sets and generate statistics. For example, you could show a sample of the most common words and phrases [16-20]. The person carries out further analysis of the data and, on its basis, makes the appropriate conclusions. There is no complete computer analysis and system training [21-33].
The following resources can be exemplified as appropriate systems: Istio.com - allows you to get advanced text analysis by word, output the top most used words and other text information. Info.seocafe.info - resource allows you to define the basic parameters of the text that designed mainly for SEO optimisation (Fig. 5).
The advantages of such resources are ease of use - the user simply enters the required text, and the system automatically processes it [34-39]. However, the disadvantage is that these systems do not allow 2 3 4 5 6
The software system functionality. The mechanism of use of functions allows describing the possibilities of this information system (Table 1, Fig. 6). To improve the work with this information, we introduce the concept of function attributes - data elements (Table 2), which will provide additional information about each function (Table 3).
Description(function value) Each program must strictly personalised to the specific person that is each user should create an account to simplify the functioning of the information system
When creating an account, the user will provide sensitive information protected from unauthorised use
by third parties.
Collecting and processing data from a profile on a social
network. Key metric filtering Identifying critical patterns in the collected data and
compiling a statistical report Based on the collected data and previous experience the refinement of the program's previous findings or
creation of new ones
Providing access to pre-analysis data for the user Identifying critical indicators of a person's character
Identification of critical indicators of a person's
temperament Identification of critical indicators of a person's
emotionality Identifying critical indicators of a person's self-esteem
Determination of critical indicators of a person's
volitional qualities Identifying critical indicators of a person's social
qualities Based on features 7-12, compare previous results with
current results and adjust them if necessary
The generalisation of the psychological state of the
individual Identifying the necessary strategies for communicating with the individual or other interaction features Identifying a study error and keeping it informed
Creating complete support for the user
The ability for certain users with the necessary authority to correct possible errors in the mechanisms
of psychological analysis or the results Typing of personality by Sigmund Freud's research and
classification Typing of personality by research and classification of
Jung Creation of the necessary reference book, which contains a description of all the psychological
characteristics of the individual Data backup function for cases where this data can be
lost (personal data, research data) The ability to interactively compare data from different
social networks The ability to personally analyse a personalised user, both based on their social media accounts and queries Based on specific preferences, search or filter people on social networks
Based on the analysis of known means of solving the problem and taking into account the features of IP, it is possible to distinguish the finite functions (Table 3).
Attribute Status (determination of the final version of the feature approval)
Priority (importance of function)
Difficulty (complexity of function implementation) Risk (the likelihood that implementing a function will cause undesirable effects such as cost increases, changes in the implementation schedule,
etc.) Stability (the likelihood that this function will change over time) Target version (the version of the product where the feature first
appears) Purpose (comments for developers that improve their understanding of development)
Description (Function Value)
Suggested.
Approved. On.
Critical.
Important. Useful
Low. Average.
High.
Low. Average.
High.
Low. Average.
High.
Version number
Comment 1.0 1.0 2.0 3.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
High High High High High High High 17 Important Included 18 Critical Included 19 Important Proposed 20 Important Proposed 21 Important Proposed 22 Critical Proposed 23 Useful Proposed 24 Important Proposed recommendations determination of errors in the study creation of help
bug fixes classification by S.
Freud Jung classification encyclopaedia data backup personal analysis interactive data comparison from different social
networks search and filter people by a specific psychological type
Low Average Average Average Average Average Average Average Low High High
High Average Average Average Average Low High High
High Average
High High High
Considering all of the above, it should be noted that, as there are currently no qualitative and comprehensive solutions on the information technology market that offer a solution to this problem, it is incredibly relevant. The purpose of research in this area is to assess the psychological state of modern society. It is possible to create severe information systems (IPs) that, based on social networks, will determine and predict the so-called "temperature" - the general state of society at a certain point in time. It will help avoid all kinds of problems related to social dissatisfaction of the population, etc., to find your application to solve a wide range of issues and tasks in various fields.
c. IP conducts data analysis; d. The user begins a new psychological study; e. IP displays detailed analysis results on the screen.
II. The user re-enters the correct person identifier.
III. If necessary, the user can ask the IP and obtain (as a hint) the complete list of possible person identifiers (for example, those that begin with a specific number or letter).
IV. The IP notifies the user of the error and cancels the input of the requested person. b. No person found
I. If the person is not found again, the IC notifies the user of the error and returns to the initial state.
II. If necessary, the user can ask the IP and obtain (as a hint) the complete list of possible person identifiers (for example, those that begin with a specific number or letter).
III. The IP notifies the user of the error and cancels the input of the requested person.
Person restricted access to their data
I. The IP queries the search for another person or returns to its original state.
II. The IP notifies the user of an access error. 6. Post-conditions: this is a list of conditions that must always be met in the case of successful execution of the main scenario (i.e., when the interests of all interested parties under item 2 are satisfied), for example: a. The session has successfully recorded in the IP database. b. The user has completed this session. c. The necessary recommendations and conclusions are presented.
d. Data on psychological analysis of personality are processed and saved in IP. 7. Special scenarios: a. Provide special access to a privileged group of users to correct malfunctions. b. Ensure that all sessions are as reliable as possible. c. Ensure that the IP user interface is localised.
d. Provide 100% data storage capability. 8. List of necessary technologies and additional devices: a. IP must be developed for all existing desktop and mobile operating systems. b. IP must be submitted as an application for all existing browsers.
c. IP should be designed as a WEB-oriented system.
A sequence diagram describes a process of psychological analysis of a person initiated by a particular user of the system. The user logs in and queries the person's search database. The IS finds the person, processes the data and returns the result. Before shutting down, the system records this session to its database (Fig. 9).
Fig. 10 shows an example of a commented UML dependency packet diagram that depicts a typical WEB-based IC architecture for working with a database and system decision logic.
We distinguish the functions of the software and count the number of factors: 1. External inputs: Three (practical recommendations, methods of psychoanalysis, ways of presenting results).
2. External outputs: Three (psychological portrait, study error information, research-based recommendations).
3. External requests: One (classification of psychological types). 4. Internal logical files: Two (preliminary analysis data, personal account data). 5. External logical files: Three (text, data from other social networks, data on Internet activity).
The resulting values are then multiplied by the complexity coefficients for each factor (according to IFPUG) and are summarised. The importance of these coefficients is given in Table. 4.
Accordingly, the considered example in Table 5 shows the parameter values. Internal logical files Exterior entrances External logical files
VAF = AF × (0.65 + 0.01 × 29) = 66.74.
The resulting VAF value can then be converted to a unit of measure (code line number, SLOC), or a performance factor can be estimated because of FP per day (Performance factor), based on which project complexity can be estimated. 4.3.
The Gantt Project software allows you to visually analyse the development of a particular software product or system and properly allocate the resources and time of project staff. The tool avoids unwanted anomalies such as untimely completion of the project, misallocation of forces and more. The primary step is to subdivide the project into sub-tasks and identify their executors (Fig. 11). a) b) Figure 11: a) List of project participants and b) subdivision of the project
After all the subtasks are created, we get the diagram shown in Fig. 12. It is also possible to review and check whether the resources in the project were allocated correctly (Fig. 13).
For all the above actions, we obtain a PERT diagram showing the sequence of software creation processes as a result (Fig. 14).
Choosing a software design methodology is a critical point in developing IP analysis of a person's psychological state (Fig. 15). The design process determines which ways and ways the system will be implemented. It describes the sequence of work and the distribution of labour. By choosing the correct methodology, you can develop optimal options for creating an information system. This IP will be geared towards Internet applications, so the choice of tools and implementation tools considered the technologies that will allow you to implement the required software product. For a long time, not all Internet technologies had a defined standard. Of course, with the advent of HTML5 and CSS3 and the release of Microsoft's MS Edge browser, the situation is changing dramatically. However, there is still a problem with the backward compatibility of sites. When choosing an architectural solution, it is necessary to use two parts, server and client. Both pieces are closely linked and function as one. However, processing of the primary process will take place in the server part (Fig. 16).
In this case, the server hardware is a remote server. There is no need to create a separate server for the project because the program does not have data processed on the server-side. In the future, its implementation is necessary. The client part will be contained on the user's device (PC, laptop, tablet or another device) and displayed in the browser. When designing a Web site, you should consider how it behaves on different devices, with varying diagonals of the screen and other browsers. Recently, tablets and smartphones have become particularly popular as the most mobile devices. Viewing the content on the therefore, the so-called cross-platform and cross-browser are used to solve these problems.
Cross-platform allowed to adapt the website to screens with small diagonals and focused on the site only the most necessary content of the result of the analysis of the person's psychological state. It reduces the load time of the web page, which is one of the critical points. Cross-platforming can be achieved through media queries and the use of "rubber" layouts. A Cross-browser is designed to make the site appear the same on all popular browsers, as each browser has its technologies for implementing specific functions. Therefore, different individual scripts and requests are used to make the site look the same in any browser. Today, the following are popular: Google Chrome; Mozilla Firefox; Opera; Safari; Internet Explorer; Microsoft Edge.
The situation will finally change with the release of Windows 10 and the new Microsoft Edge browser. The new browser promises to get around even the current leader Google Chrome. In addition, it will support all the modern WEB standards. The next step is to choose a software solution.
Certainly, hypertext mark-up language (HTML) and cascading style sheets (CSS) will be used when designing any web site. As virtually all browsers have recently started to support the basic standards, it is worth paying attention to versions of these technologies, namely HTML5 and CSS3, which prevent the use of outdated technologies such as Flesh on the site. Writing programs is stopped in JavaScript, which has been popular with web developers in recent years. Therefore, the following technologies are selected to create the page layout (otherwise called front-end): HTML5, CSS3, and JavaScript. To create a back-end, you should use technologies such as PHP programming language and MySQL commands. The implementation of the software is performed using the following three software components: HTML, CSS and JavaScript. HTML is required to create the site structure. CSS is a language that defines a website's visual appearance; JavaScript is a programming language needed to implement what cannot achieved in CSS and provide site interactivity. The main file is the index.html file, which is the site's main page (Fig. 19).
All major blocks of the site will placed in so-called containers. They are required to centre the content on a web page (Fig. 20-21). They are described all in the main CSS file.
JavaScript must use to create features such as user authorisation and word statistics, as the visual appearance and content of some of the blocks on the site changes during the execution process. This cannot implemented with cascading styles only. In addition, certain values and identifiers are created in the process, which are necessary for the proper functioning of the program. a) Figure 19: Web site structure
b)
After creating all the necessary features, we get a site template, which, after testing, is ready for use (Fig. 22-23). Fig. 24 shows a diagram of an IC database for the analysis of a person's psychological state. By analogy with the definition of the amount of information, we introduce the following concepts. The level of awareness of the decision-maker (ODA), i.e. the doctor, indicates the expert's level of knowledge about the subject of analysis or research. a) b) Figure 22: a) User authentication and b) File selection process
Quantitatively, the level of awareness of ODA will be characterised by the magnitude of the change in the level of uncertainty of knowledge of obtaining information. Understanding ATS means a difference in the level of uncertainty about the situation or subject of analysis of receiving information as a result. With the receipt of data, the level of uncertainty of the problem may decrease if the notification is accurate but may increase if it is deliberately distorted or untrusted (i.e. not confirmed by experience, calculations, documents or otherwise). In addition, the uncertainty of the situation can be assessed based on the objectives of systematic analysis, particularly in terms of determining the extent and level of risk. For each condition, the following types of uncertainty of knowledge characterise: associated with the possibility of a situation; described by the quality of available and new information received; related to the degree of impact of a problem on the level of risk.
It follows that raising the level of awareness of ODA does not always lead to the disclosure of the
uncertainty of the occurrence of a particular situation, as it was accepted during the formation of relation
(
We analyse the impact of the quality of information on the level of awareness of ODA. It should be noted that the assessment of the quality of data is the least studied in both computer science and other disciplines, one way or another related to information: the theory of optimal management, the idea of decision making, and so on. Today there is no accepted system of indicators for assessing the quality characteristics of information. Therefore, it is not advisable to dwell on the analysis of the various approaches to formalising them since they do not apply to the solution of most practical system analysis problems. Here are only the essential qualitative properties of information crucial to system analysis tasks, particularly for assessing the degree and level of risk in common, freelance, and critical situations. The definitions in Table 7 should be taken into account when defining ODA awareness indicators [21
= − ∑ =1 .
(
a property that reflects the presence of several alternative descriptions of the Inaccuracy a property indicating that there is a specific interval of tolerances or errors in measurements or calculations in the quantitative parameters and/or
qualitative characteristics of the situation description Incomplete a property that reflects the presence of information gaps in the description of
the situation (something missed, not described enough, etc.) Blurred
a property that characterises the vagueness of describing a situation where it Timeliness property that characterises the relationship in time between the moment of is impossible to accurately determine the presence or absence of a particular property or its exact quantitative characteristic (for example, it is unbelievable to quantify concepts such as comfortable weather, favourable situation
accurately - their description is subjective, vague) occurrence of an event and the moment of receipt of information about it; If
ODA does not have enough time to form and make decisions based on the
information received, then it is untimely a property that reflects the presence of quantitative data or qualitative characteristics that do not correspond to the actual state of the situation
a property that indicates the presence of quantitative or qualitative characteristics that have meaning or content that contradicts other data 1 2 3 4 5 6 7
Unreliability
Controversy
Let's analyse some techniques and the essence of the uncertainties of the occurrence of situations.
We will assume that the level of non-awareness is the uncertainty of knowledge about the emergence
of an alternative from the predicted set of problems. The anticipation of expertise can be estimated
based on different approaches. Let the set of possible situations Ms is discrete, and each element Si of
the set Ms characterises a certain probability pi for = 1, Then the value of non-awareness Hs will
be defined as the level of uncertainty of information about Ms. Therefore, we have a condition similar
to that for formula (
Note that for equally probable events = 1/ and = [21-30].
= { | = 1, } under the influence of many uncontrolled risk factors , the staff situation can turn into a critical, emergency or catastrophic [21]. Such a transition may occur over some time, the duration of which is unknown at a priori and which depends on the number, properties and duration of the influence of the factors ∈ . It is necessary to determine such a permissible period 0 for the formation and implementation of a solution for which the probability of a situation transition to a critical, emergency or catastrophic one will not exceed the set value =
. The number of risk factors and situations will be given in Table 8, where the "+" sign means that when the relevant factor influences the staff situation becomes critical, emergency or catastrophic, and the "-" sign does not affect the case. Note that the method and algorithm for solving the problem are applicable to finite values i and j. reliability ratio [21]:
The likelihood of a situation transitioning under the influence of a factor ∈ ; ∈ [1; 7] in a critical, emergency or catastrophic situation depends on the change in the timing of completeness , and timeliness
of ODA awareness. The probability ij of such an event determines the
= 1 − [1 + ( )]; ( ) =
( )
( )
( ).
=1
= − ∑
(
u ta i a fa D + + fo te t ce ir n p i
o c se r bA app sp s i l e fko tvan tre u a ca l e r e
L re ilt + + + + Risk factors that affect the transition of a staff situation to a critical or catastrophic one c
a in tx s e c e e re i
t r c i
n c
e o c ra i
d n p u I
g + +
To make a decision, it is necessary to find a rational compromise between levels the time for its formation and implementation [21]. The indicators of completeness of ODA awareness increase over time and are defined by the following conditions [21]: ,
, to shorten and reliability 1, 1, ̂ (1 + ), 0 < ̂ (1 + ) < 1, ̂ (1 + ) ≥ 1;
̂ (1 + ), 0 < ̂ (1 + ) < 1, ̂ (1 + ) ≥ 1.
At the same time as the time of influence of factors ∈ decreases the level of the indicator of timeliness of awareness according to its properties which characterises the ratio ( ) = { (1 − 2), 0 < 2 < 1, ̂
2 ≥ 1.
Therefore, the length of time for the formation, adoption and implementation of the ODA decision is reduced to prevent the transition of the investigated situation to a critical, emergency or catastrophic one. The coefficients , , characterise the dynamics of changes in awareness indicators. They are determined by the dependencies [21]: 0, ̑ 0, 0,
= { ( ̂ 0, = { ̂ 0,5, 0 <
≤ 1,
> 1; + ) ̂ 10−5, 0 < ̂ +
≤ 1, ̂
> 1. ̂ ̂ 0,05, 0 < ̂
≤ 1, ̂ > 1.
The values ̂ , ̂ , ̂
are preliminary estimates of the relevant indicators, which experts determine at the time of detection of a freelance model of operation of the system [21], and the coefficients ̂ characterise the influence level of each of the factors ∈ ; ∈ [1; 7] on the properties of situations , ∈ [1; 4]. The values of indicators ̂ , ̂
and coefficient ̂ are given in Table 9. , ̂
and coefficient̂
To determine the length of the allowable period 0 = [ 1; 2], where both 1 and 2 the lower and the upper bounds of the interval solve the inequality: 0 ≤ 1 − (1 + (1 + )(1 + )(1 − 2)) ≤ Valid intervals for decision formation ij 10 4 0,375 0,627 1,004 0,596 0,456
0,801 0,711 0,955 0 0,071 0,095 0 0 0,854 0 0,084 0 0 0 0 0,596 0,058 0,033 0,063 1,00 0,058 0,044
0,080 0,855 0,493 0,353 0,664 0,805 0 0 0 0 0,709 0 0 0,461 0 0 0,529 0,496 2 0,570 0,694 0 0
4
For the situation, the time allowed for the formation, decision-making and implementation of the decision should not exceed T1=34.5; for S2-T2=32.6; for S3-T3=35.9; for S4-T4=39.9.
Software testing is performed modularly. It is a static analysis of the program, which is the construction of a graph (Fig. 25), the elements of which are the parts of the software to be tested.
Testing of the tool will be carried out in a monolithic way (simultaneous combination of all modules that form the system into one testing complex). In the general case, if the function is not performed, it will be blocked and will need further correction. This testing method is most appropriate since there is no need to break it down into separate complexes. After developing the interface part and testing, a general overview of the resource's performance is provided. When accessing the resource, the user gets to the authorisation page (Fig. 26-27). Here is a general description of the help and the actual authorisation field. If the authorisation fails, the user will see an error.
After successful authorisation, the user is taken to the resource's home page, where his personal information will be displayed and a block where the user can select the file for analysis and analyse it. To analyse the text, select the text file and click the "Analyse!" Button.
File analysis in the text, as a result, the ten most used words in the text will be displayed. In addition, based on the comparison of keywords, the person's psycho-emotional state will be deduced. Therefore, the site contains only the necessary information and does not create difficulties for the user when working with it. All functions are clear and straightforward.
In the course of the work, an information system is developed, through which it is possible to conduct a psychological analysis of a person using his messages from social networks. The system helps to automate the process of gathering information and obtaining results. The subject area is analysed, various information systems based on a particular principle are considered. A description of the system requirements according to the RUP methodology is given, a detailed description of the main functions of the system, an algorithm of operation, and side effects. The necessary software environment description in which the information system is developed is made and the implementation of the software. The information system meets all modern requirements and allows users to analyse the text and formulate the necessary conclusions.
[14] B. Rusyn, V. Vysotska, L. Pohreliuk, Model and architecture for virtual library information system, in: Proceedings of the International Conference on Computer Sciences and Information Technologies, CSIT, 2018, pp. 37-41. [15] B. Rusyn, V. Lytvyn, V. Vysotska, M. Emmerich, L. Pohreliuk, The Virtual Library System Design and Development, volume 871 of Advances in Intelligent Systems and Computing, 2019, pp. 328-349. [16] L. Chyrun, I. Kis, V. Vysotska, L. Chyrun, Content monitoring method for cut formation of person psychological state in social scoring, in: Proceedings of the International Conference on Computer Sciences and Information Technologies, CSIT, 2018, pp. 106-112. [17] V. Vasyliuk, Y. Shyika, T. Shestakevych, Information System of Psycholinguistic Text Analysis, volume Vol-2604 of CEUR workshop proceedings, 2020, pp. 178-188. [18] S. Fedushko, M. Davidekova, Analytical service for processing behavioral, psychological and communicative features in the online communication, volume 160 of The International Workshop on Digitalization and Servitization within Factory-Free Economy (D&SwFFE), 2019, pp. 509-514. [19] M. Z. Zgurovsky, N. D. Pankratova, System analysis: Theory and applications. Springer Science & Business Media, 2007. [20] M. Z. Zgurovsky, Y. P. Zaychenko, The fundamentals of computational intelligence: System approach. Springer International Publishing, 2017. [21] M. Z. Zgurovsky, N. D. Pankratova, Systems analysis. Problems, methodology, applications [in
Russian], 2010.
[22] M. Z. Zgurovsky, V. S. Mel'nik, P. O. Kasyanov, Evolution Inclusions and Variation Inequalities
for Earth Data Processing I: Operator Inclusions and Variation Inequalities for Earth Data
Processing, volume 24 of Springer Science & Business Media, 2010.
[23] M. Z. Zgurovsky, A. D. Gvishiani, K. V. Yefremov, A. M. Pasichny, Integration of the Ukrainian
science into the world data system, volume 46(
Problems and Challenges. Springer, 2018. [26] M. Z. Zgurovsky, A. A. Pavlov, The four-level model of planning and decision making, in:
Combinatorial Optimization Problems in Planning and Decision Making, 2019, pp. 347-406.
[27] M. Z. Zgurovsky, V. G. Totsenko, V. V. Tsyganok, Group incomplete paired comparisons with
account of expert competence, volume 39(
International Publishing, 2016. [29] V. Lytvynenko, I. Lurie, J. Krejci, M. Voronenko, N. Savina, M. Ali Taif. Two Step Density-Based Object-Inductive Clustering Algorithm. Proceedings of the Second International Workshop on Modern Machine Learning Technologies and Data Science (MoMLeT& DS-2019), Shatsk, Ukraine, June 2-4, 2019, pp. 117-136. ISSN 1613-0073. URL: http://ceur-ws.org/Vol-2386/ [30] I. Lurie, V. Lytvynenko, S.e Olszewski, M. Voronenko, W. Wóicik, O. Boskin, U. Zhunissova, M.
Sherstiuk, Application of Inductive Bayesian Hierarchical Clustering Algorithm to Identify Brain Tumors, volume 1246 of Advances in Intelligent Systems and Computing, 2021, pp. 567-584 DOI: 10.1007/978-3-030-54215-3_36 [31] V. Demchenko, S. Olszewski, M. Voronenko, E. Zaets, N. Savina, I. Lurie, V. Lytvynenko, Modeling and predicting the organochlorine pesticides concentration in the child's body based on their accumulation in the mother's body, volume 2631 of CEUR Workshop Proceedings, 2020, pp. 419-432. [32] S. Fedushko, M. Gregus, T. Ustyianovych, Medical card data imputation and patient psychological and behavioral profile construction, in: Proceedings of the 9th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH), 160, 2019, pp. 354-361. [33] V. Lytvyn, V. Vysotska, I. Peleshchak, T. Basyuk, V. Kovalchuk, S. Kubinska, L. Chyrun, B.
Rusyn, L. Pohreliuk, T. Salo, Identifying Textual Content Based on Thematic Analysis of Similar Texts in Big Data, in: Proceedings of the International Conference on Computer Sciences and Information Technologies, CSIT, 2019, pp. 84-91. [34] V. Danylyk, V. Vysotska, V. Lytvyn, S. Vyshemyrska, I. Lurie, M. Luchkevych, Detecting Items with the Biggest Weight Based on Neural Network and Machine Learning Methods, volume 1158 of Communications in Computer and Information Science, Springer, Cham, 2020, pp. 383-396.
DOI: https://doi.org/10.1007/978-3-030-61656-4_26 [35] O. Duda, N. Kunanets, O. Matsiuk, V. Pasichnyk, N. Veretennikova, A. Fedonuyk, V. Yunchyk, Selection of Effective Methods of Big Data Analytical Processing in Information Systems of Smart Cities, volume Vol-2631 of CEUR Workshop Proceedings, 2020, pp. 68-78. [36] A. Lutskiv, N. Popovych, Big Data Approach to Developing Adaptable Corpus Tools, volume
Vol-2604 of CEUR workshop proceedings, 2020, pp. 374-395. [37] O. Naum, L. Chyrun, O. Kanishcheva, V. Vysotska, Intellectual System Design for Content Formation, in: Proceedings of the International Conference on Computer Sciences and Information Technologies, CSIT, 2017, pp. 131-138. [38] V. Lytvyn, P. Pukach, І. Bobyk, V. Vysotska, The method of formation of the status of personality understanding based on the content analysis, volume 5/2(83) of Eastern-European Journal of Enterprise Technologies, 2016, pp. 4-12. [39] V. Lytvyn, V. Vysotska, V. Shatskykh, I. Kohut, O. Petruchenko, L. Dzyubyk, V. Bobrivetc, V.
Panasyuk, S. Sachenko, M. Komar, Design of a recommendation system based on Collaborative
Filtering and machine learning considering personal needs of the user, volume 4(
Chyrun, D. Peleshko, T. Rak, The Intellectual System Development of Distant Competencies Analysing for IT Recruitment, volume 1080 of Advances in Intelligent Systems and Computing IV, Springer, Cham, 2020, pp. 696-720. [47] O. Lozynska, V. Savchuk, V. Pasichnyk, Individual Sign Translator Component of Tourist Information System, volume 1080 of Advances in Intelligent Systems and Computing IV, Springer Nature Switzerland AG, Springer, Cham, 2020, pp. 593-601. [48] N. Antonyuk, M. Medykovskyy, L. Chyrun, M. Dverii, O. Oborska, M. Krylyshyn, A. Vysotsky, N. Tsiura, O. Naum, Online Tourism System Development for Searching and Planning Trips with User's Requirements, volume 1080 of Advances in Intelligent Systems and Computing IV, Springer Nature Switzerland AG, 2020, pp. 831-863. [49] V. Lytvyn, V. Vysotska, Y. Burov, V. Hryhorovych, Knowledge novelty assessment during the automatic development of ontologies, in: Proceedings of the IEEE 3rd International Conference on Data Stream Mining and Processing, DSMP, 2020, pp. 372-377. [50] Y. Burov, V. Lytvyn, V. Vysotska, I.a Shakleina, The Basic Ontology Development Process Automation Based on Text Resources Analysis, in: Proceedings of the IEEE 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT, 2020, pp. 280-284. [51] M. Emmerich, V. Lytvyn, V. Vysotska, V. Basto-Fernandes, V. Lytvynenko, Preface: Modern Machine Learning Technologies and Data Science (MoMLeT+DS 2020), volume Vol-2631 of CEUR Workshop Proceedings, 2020. [52] V. Vysotska, Ukrainian Participles Formation by the Generative Grammars Use, volume Vol-2604 of CEUR workshop proceedings, 2020, pp. 407-427. [53] O. Bisikalo, V. Vysotska, V. Lytvyn, O. Brodyak, S. Vyshemyrska, Y. Rozov, Experimental Investigation of Significant Keywords Search in Ukrainian Content, volume 1293 of Advances in Intelligent Systems and Computing V, 2021, pp. 3-29. [54] O. Bisikalo, V. Vysotska, Linguistic analysis method of Ukrainian commercial textual content for data mining, volume Vol-2608 of CEUR Workshop Proceedings, 2020, pp. 224-244. [55] B. Rusyn, L. Pohreliuk, A. Rzheuskyi, R. Kubik, Y. Ryshkovets, L. Chyrun, S. Chyrun, A.
Vysotskyi, V. B. Fernandes, The Mobile Application Development Based on Online Music Library for Socialising in the World of Bard Songs and Scouts' Bonfires, volume 1080 of Advances in Intelligent Systems and Computing IV, 2020, pp. 734-756.