The method of formation of the status of personality understanding based on the content analysis

The approach is proposed to developing an information system of determining the psychological state of personalities based on the five personality dispositions (extraversion/introversion, amiability, integrity, neuroticism, openness to experience), which is based on the content analysis of the Internet resources where users leave their mark (social networks, forums, chats, etc.). In general, to form the status of psychological state of a personality based on the content analysis, it is necessary to solve four problems. First, it is necessary to collect content from various sources from the Internet. Then it is necessary to process it at the initial level (remove the tags, auxilary words, signs, special symbols, hyperlinks, pictures, etc. from the text). Then the content is filtered (to identify spam, detect duplication, format the content, etc.) and sorted out (comments to the comments, likes, posts) according to the statistics over a specific period. The last stage is conducting the content analysis of collected information, which is categorized by the stop-words (markers). To determine the psychological dispositions of a personality we implemented the developed method of the search and analysis of the marked words in the English and Ukrainian languages. We used the Potter stemming, lemmatising and the modified Potter stemming for the Ukrainian texts, designed by the authors. The tables of correlation between the marked words and psychological dispositions were developed. The information system is created for determining the psychological state of personality, based on the developed approach and the methods of the content processing. The system operates by analyzing the messages from the users in a social network based on the traits of the "Big Five". The system is designed in the form of a desktop program, which is the Internet service at the same time, and allows analyzing the psychological state of a particular user of a social network by his/her messages. All collected results are stored in the database. The results are displayed in the form of percent ratio for each trait, the number of tweets, as well as the most frequently used words related to these traits. Potential users of such systems are consulting and marketing companies. The collected and analyzed information on users may be used in hiring or promotion of products/services. Automated compilation of the personality models of users is helpful for social networks and Web services. It improves the quality and efficiency of context advertising, referral systems, recommendations and dating services. The in-depth knowledge of the audience is crucial for business and recruiting. The approbation of functioning of the constructed system was conducted. The results of the work of the system are satisfactory. Such an information system is recommended to use for searching employees for certain positions. Automated analysis of messages of users in a social network to form the status of psychological state of a personality based on the content analysis significantly reduces the time of finding a potentially promising employee among those applied taking into account his/her psychological portrait for a specific position.

[1]  Mansor H. Ibrahim,et al.  Business cycle and bank lending procyclicality in a dual banking system , 2016 .

[2]  Satoru Ohta,et al.  Performance Degradation Detection of Virtual Machines Via Passive Measurement and Machine Learning , 2014, Int. J. Adapt. Resilient Auton. Syst..

[3]  Cansu Cigdem Aydin,et al.  OPEN SOURCE LEARNING MANAGEMENT SYSTEMS IN DISTANCE LEARNING , 2010 .

[4]  Savvas A. Tassou,et al.  Food transport refrigeration – Approaches to reduce energy consumption and environmental impacts of road transport , 2009 .

[5]  Erik Duval,et al.  Learning Analytics Dashboard Applications , 2013 .

[6]  David Richard Moore,et al.  E-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning , 2006 .

[7]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[8]  Margaret L. Kern,et al.  Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.

[9]  Indrajit Mukherjee,et al.  A Modified Desirability Function Approach for Mean-Variance Optimization of Multiple Responses , 2013, Int. J. Softw. Sci. Comput. Intell..

[10]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[11]  Peter A. Rosen,et al.  Social Networking Websites , Personality Ratings , and the Organizational Context : More Than Meets the Eye ? , 2012 .

[12]  María-Isabel Encinar,et al.  Evolutionary efficiency in economic systems: A proposal , 2016 .

[13]  Luis M. Serra,et al.  Design of Evaporation Systems and Heaters Networks in Sugar Cane Factories Using a Thermoeconomic Optimization Procedure , 2007 .

[14]  Noam Lior,et al.  Development of a novel combined absorption cycle for power generation and refrigeration , 2007 .

[15]  Ezzeddine Hadj-Taïeb,et al.  Influence of pump starting times on transient flows in pipes , 2011 .

[16]  Elizabeth Colucci,et al.  E-Learning in European Higher Education Institutions: Results of a Mapping Survey Conducted in October-December 2013. , 2014 .

[17]  Ahmed Bellagi,et al.  Experimental and thermodynamic investigation of an ammonia/water diffusion absorption machine , 2014 .

[18]  Christina Delimitrou,et al.  Paragon: QoS-aware scheduling for heterogeneous datacenters , 2013, ASPLOS '13.

[19]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[20]  Meiko Jensen,et al.  Challenges in data science: a complex systems perspective , 2016 .

[21]  Dazhuan Wu,et al.  Numerical Simulation of the Transient Flow in a Centrifugal Pump During Starting Period , 2010 .

[22]  Bernd Langhans.  Crystallization - a central competence, the key to success , 2004 .

[23]  Olga Serdiuk,et al.  Development of executive system architecture of the converting class , 2016 .

[24]  Jérôme David,et al.  Detection and Transformation of Ontology Patterns , 2009, IC3K.

[25]  Francesca A. Lisi,et al.  Nonmonotonic Onto-Relational Learning , 2009, ILP.

[26]  Dazhuan Wu,et al.  Experimental and numerical study of transient flow in a centrifugal pump during startup , 2011 .

[27]  Maria Prandini,et al.  The scenario approach for systems and control design , 2009, Annu. Rev. Control..

[28]  Gianluca Demartini,et al.  The Bowlogna ontology: Fostering open curricula and agile knowledge bases for Europe's higher education landscape , 2013, Semantic Web.

[29]  Avi Levy,et al.  The influence of diffusion absorption refrigeration cycle configuration on the performance , 2007 .

[30]  Mahdi Bashiri,et al.  A new desirability function-based method for correlated multiple response optimization , 2015 .

[31]  Mohamed Medhat Gaber,et al.  Automatic Content Related Feedback for MOOCs Based on Course Domain Ontology , 2014, IDEAL.

[32]  B. Kazansky,et al.  STUDIES ON CORROSION RESISTANCE OF COATINGS FORMED BY PLASMA ELECTROLYTIC OXIDATION ON ALUMINUM ALLOYS , 2011 .

[33]  Michael J. North,et al.  Parallel agent-based simulation with Repast for High Performance Computing , 2013, Simul..

[34]  Bikash Sharma.  Applications of Data Mining in the Management of Performance and Power in Data Centers , 2009 .

[35]  Ji-Ping Huang,et al.  Risk-Return Relationship in a Complex Adaptive System , 2012, PloS one.

[36]  Robin Mason,et al.  e-Learning and Social Networking Handbook: Resources for Higher Education , 2008 .

[37]  Ibrahim Dincer,et al.  Developments in Absorption Refrigeration Systems , 2016 .

[38]  Marcia J. Simmering,et al.  E-learning: emerging uses, empirical results and future directions , 2003 .

[39]  Michael Brückner,et al.  Context-Aware Tourism Recommender System Using Temporal Ontology and Naïve Bayes , 2015 .

[40]  Charles Mbohwa,et al.  To design and implement a reliable sugar evaporation control system that will work in an energy saving way , 2015 .

[41]  Igor Lutsenko.  Optimal control of systems engineering. Development of a general structure of the technological conversion subsystem (Part 2) , 2015 .

[42]  Karmen Pažek,et al.  The Development of Sugar Beet Production and Processing Simulation Model – a System Dynamics Approach to Support Decision-Making Processes , 2014 .

[43]  Aniruddha S. Gokhale,et al.  Towards a performance interference-aware virtual machine placement strategy for supporting soft real-time applications in the cloud , 2014, REACTION.

[44]  A. Lehnberger,et al.  Falling-film evaporator plant for a cane sugar factory: Presentation of the concept and operating results* , 2014 .

[45]  Dzmitry Kliazovich,et al.  Profiling cloud applications with hardware performance counters , 2014, The International Conference on Information Networking 2014 (ICOIN2014).

[46]  José Maria Parente de Oliveira,et al.  Concept maps as the first step in an ontology construction method , 2013, Inf. Syst..

[47]  H. Tsukamoto,et al.  Transient characteristics of a centrifugal pump during stopping period , 1982 .

[48]  Michael Brückner,et al.  A Semantic Similarity Assessment Tool for Computer Science Subjects Using Extended Wu & Palmer’s Algorithm and Ontology , 2015 .

[49]  P. W. L. Lyne.  Decision support systems for sugarcane production managers. , 2012 .

[50]  Avi Levy,et al.  Performance of diffusion absorption refrigeration cycle with organic working fluids , 2009 .

[51]  S. S. Litvin,et al.  Development of alternative technical systems , 1991 .

[52]  Shoubin Dong,et al.  Dynamic VM Consolidation for Energy-Aware and SLA Violation Reduction in Cloud Computing , 2012, 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies.

[53]  Kevin O. Samms.  Data Mining and Analysis , 2015 .

[54]  Do Guen Yoo,et al.  Rehabilitation Priority Determination of Water Pipes Based on Hydraulic Importance , 2014 .

[55]  Oded Maler,et al.  Approximating the Pareto Front of Multi-criteria Optimization Problems , 2010, TACAS.

[56]  Steven E. Higgins,et al.  Effects of traditional, blended and e-learning on students' achievement in higher education , 2013, J. Comput. Assist. Learn..

[57]  Juan M. Belman-Flores,et al.  Review of diffusion–absorption refrigeration technologies , 2014 .

[58]  Siddharth Jain,et al.  A Comparative Study for Cloud Computing Platform on Open Source Software , 2014 .

[59]  Jing Zhang,et al.  MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement , 2015, Int. J. Distributed Sens. Networks.

[60]  A. S. Dovbysh,et al.  Information-Extreme Method for Classification of Observations with Categorical Attributes , 2016 .

[61]  Olga Sanginova.  Comparative analysis of some computational schemes for obtaining a compromise solution , 2015 .

[62]  S. Haaland.  Simple and Explicit Formulas for the Friction Factor in Turbulent Pipe Flow , 1983 .

[63]  Ming Yang,et al.  Simulation of Multi-Agent based Cybernetic Transportation System , 2008, Simul. Model. Pract. Theory.

[64]  V. V. Moskalenko,et al.  Learning Decision Making Support System for Control of Nonstationary Technological Process , 2016 .