การวิเคราะห์ทัศนคติเกี่ยวกับการจัดการภัยพิบัติในประเทศไทยบนทวิตเตอร์

Abstract

The purpose of this research was to study the needs and attitudes of Thai people towards disaster management in each situation of the Thai government. The top three hashtags on Twitter are a chemical leak, The King Kaew factory fire, and Flooding in Ubon. As a result, Sentiment Analysis knowledge was applied. The data obtained will be used with two data sets: all data sets and the data set that will be used to group the data (Clustering) to obtain a more specific data set that only conveys the user's attitude. The researchers created four models: Logistic Regression, Decision Tree, Random Forest and Support Vector Machine (SVM). To compare the two dataset’s sentiment analysis predictions. Clustering the messages first can help yield better results. However, the data is reduced in size as a result of segmentation. As a result, employing The Synthetic Minority Oversampling Technique (SMOTE) aids in the resolution of the problem. The best model across all hashtags is Decision Tree, which has an Accuracy Score of 93% in #Chemical Leaks and 91% in #KingkaewFire and #Flooding in Ubon. Word Cloud was used to study requirements or what the most people say, both positively and negatively. The words 'government' and 'politics' were discovered to be frequently used in every hashtag's positive and negative connotations. This study concludes that the Thai government's disaster management is insufficient to meet the needs of the Thai people because shows primarily on the negative.