MACHINE LEARNING AND ANALYSIS OF TWITTER DATA: IDENTIFYING BUSINESS TRENDS AFTER COVID-19

Abstract

With the Covid-19 epidemic, there has been a great change in the routines of social and business life. These changing routines have brought with them new needs and demands. In order for business life to adapt to this new order and develop new strategies, current trends should be analyzed. In this study, the most demanded business trends on Twitter after Covid-19 were analyzed by machine learning. Textual expressions obtained through Twitter are converted into data by methods such as natural language processing. Analyzing these data correctly makes it possible to obtain important information that will create a roadmap about the targeted issues. Within the scope of the research, a total of 48765 tweets with high impact were selected. Word frequency analysis was applied to the total number of tweets belonging to the determined business trends. Within the scope of the research, textual expressions obtained through twitter platforms were converted into data by natural language processing method. In addition, a word analysis model based on SVM, one of the machine learning algorithms, was used. As a result of the analysis; online food services, online sales specialist, remote working, healthcare professionals, personal coaching, online training and repairman have emerged as popular lines of business. Key words: Machine Learning, Trend Jobs, Neural Networks, Twitter, SVM, Covid-19