SENTIMENT ANALYSIS OF TOURISM-RELATED TWEETS DURING COVID-19 OUTBREAK THROUGH MACHINE LEARNING TECHNIQUES

Abstract

Covid-19 virus which is effective all the world and is a global pandemic also affected tourism choices in Turkey. In this study, sentiment analysis study was conducted over the tourism hahstagli (#turizm) Turkish tweets posted between April and August 2020. The data was obtained from the Twitter API application. 9678 messages collected in this process were structured over the necessary pre-processing and transformation processes and made ready for analysis as 4202 messages, and the messages were labeled in three categories (neutral, positive and negative) according to the emotion expressions they contain.Classification performances were compared using Machine Learning algorithms (Logistic Regression Analysis, Decision Tree, Multinominal Naive Bayes Analysis, Cluster Analysis (k-Nearest Neighbor), Support Vector Machines and Random Forests), which are frequently used in sentiment analysis studies. As a result, Logistic Regression model was found to be the most successful model.