Analysis of unstructured text data for a person social profile

The greatest scientific interest for analysts are Internet open social data, because it has a direct link with all kinds of human activity. However, these data are not suitable for the application in its original form. Information should be presented in a structured, convenient, human-readable form which is called a social profile. The social profile building is carried out through the analysis of the filtered Internet open source data. Analysis of personal profile data is achieved through the use of mathematical set theory, Big Data software, NoSQL data stores and analytic tools for social media. This article discusses methods of unstructured textual data analysis in relation to a social profile. Special attention is given to the search of implicit dependences in texts using visual analysis and natural language processing means. Phase of the textual data analysis is the most important in terms of results and complicated to implement. There is the possibility to partially automate the process of information analyzing through the use of visual analysis, natural language processing (NLP), neural networks and specialized algorithms. Resulted data provide a detailed in-depth review of the social profile entities and relations. It can be used in further deeper social researches.