REALIZATION OF THE DECISION-MAKING SUPPORT SYSTEM FOR TWITTER USERS’ PUBLICATIONS ANALYSIS

Abstract

Context. The paper emphasizes the need for a decision-making system that can analyze users’ messages and determine the sentiment to understand how news and events impact people’s emotions. Such a system would employ advanced techniques to analyze users’ messages, delving into the sentiment expressed within the text. The primary goal is to gain insights into how news and various events reverberate through people’s emotions.
 Objective. The objective is to create a decision-making system that can analyze and determine the sentiment of user messages, understand the emotional response to news and events, and distribute the data into clusters to gain a broader understanding of users’ opinions. This multifaceted objective involves the integration of advanced techniques in natural language processing and machine learning to build a robust decision-making system. The primary goals are sentiment analysis, comprehension of emotional responses to news and events, and data clustering for a holistic view of user opinions.
 Method. The use of long-short-term memory neural networks for sentiment analysis and the k-means algorithm for data clustering is proposed for processing large volumes of user data. This strategic combination aims to tackle the challenges posed by processing large volumes of user-generated data in a more nuanced and insightful manner.
 Results. The study and conceptual design of the decision-making system have been completed and the decision-making system was created. The system incorporates sentiment analysis and data clustering to understand users’ opinions and the sentiment value of such opinions dividing them into clusters and visualizing the findings.
 Conclusions. The conclusion is that the development of a decision-making system capable of analyzing user sentiment and clustering data can provide valuable insights into users’ reactions to news and events in social networks. The proposed use of longshort-term memory neural networks and the k-means algorithm is considered suitable for sentiment analysis and data clustering tasks. The importance of studying existing works and systems to understand available algorithms and their applications is emphasized. The article also describes created and implemented a decision-making system and demonstrated the functionality of the system using a sample dataset.