Intelligent system for clustering users of social networks based on the message sentiment analysis

The main objective of this article is the analysis of the intelligent system for clustering users of social networks based on the messages sentiment analysis. The main goal of this intelligent system is to form a general image of the user of the system by analyzing the sentiment of the data of the user's social networks and their subsequent clustering. An intelligent system was designed, which, using the Identity and Access/Refresh JWT token algorithms, provides fast and maximally secure registration, authentication and processing of various system user sessions. The main approaches to the sentiment analysis of user messages and other data of various types are described, the principles of LSTM implementation of a recurrent neural network are described, which is very convenient for data analysis, because it works well and remembers the context of messages in the necessary time intervals, which increases the meaningfulness factor of the data analyzed according to the user of the intelligent system. General modern approaches to clustering and the most suitable clustering algorithm k-means is also described, since we will work with an undetermined amount of data each time, which can change significantly according to each individual user, the number of clusters and data processing will change because of this. Due to this, as a result of the work, the creation of a general image of the system user was described thanks to its comprehensive analysis, which made it possible to analyze users and display the corresponding results.