The subject of research in this paper is advanced analytics of social media in predicting business crises. The primary goal of this thesis is to show the advantages of using modern social media analytics to predict business crises. The secondary objective refers to the demonstration of the functioning of the selected social media analysis tool for crisis prediction. The empirical part of the paper is based on the research of the selected analytical tool of social media in crisis forecasting. There are many tools available in the field of social media analytics. Each of them has its own applications, advantages and limitations. Also, each is suitable for specific social media platforms. Tweeter's (in 2023, the name Tweeter changes to X) system for tracking, analyzing and understanding tweets related to a specific topic was approved for analysis. Two essential techniques can be considered for data processing on Twitter, the first is called term association, and the second is related to sentiment analysis, which is crucial from the aspect of predicting a business crisis. The Twitter API is used for data analysis, and through Twitter's big data analysis process, companies can gain valuable information about data patterns and text sentiment, which is key to predictive analytics for predicting business crises using social media.