RESEARCHING THE PROCESS OF SOCIAL MEDIA AND TEXTUAL DATA ANALYZING FROM OPEN-ACCESS DATA SOURCES
Background. The greatest scientific analytical interest is drawn by open access social online data, as they are directly linked to all kinds of human activity. However, the initial form of such data is rather unsuitable for automated applied processing and should be presented in a structured, convenient, human-readable form - a social profile. Social profile building is carried out through analyzing filtered initial online data from open sources. Dynamically unstructured data, including textual and multimedia information, cannot be handled by classical analytic means. It is necessary to define new analytical methods and approaches depending on types of information for the most effective and full use of initial data. Materials and methods. The task of personal social profile data analysis is achieved through the use of mathematical tools of the set theory, Big Data software and NoSQL data storages, analytic tools for social media, as well as modern methods for analyzing multimedia data. Results. It is suggested to divide initial social profile data into static and dynamic parts. The article considers methods of unstructured textual social profile data analysis, describes a technology of searching implicit dependences in texts using visual analysis and natural language processing means, as well as offers a review of techniques for analyzing multimedia content (graphics, sound). Conlusions. The stageof textual and multimedia social profile data analysis is the most important in terms of results and quite complicated to implement. There is a possibility to partially automate the process of information analyzing through the use of visual analysis, natural language processing (NLP), neural networks and specialized algorithms. Thedata obtained provide a detailed in-depth review of social profile entities and their relations. It can be used for further deeper social researches.