TY  - JOUR
T1  - Sentiment Analysis of Foot-and-Mouth Disease Using Tweet Text-Mining Technique
AU  - Chae, Heechan 
AU  - Lee, Jonguk 
AU  - Choi, Yoona 
AU  - Park, Daihee 
AU  - Chung, Yongwha 
JO  - KIPS Transactions on Software and Data Engineering
PY  - 2018
DA  - 2018/1/1
DO  - 10.3745/KTSDE.2018.7.11.419
KW  - Text Mining
KW  - sentiment analysis
KW  - FMD
KW  - Twitter
KW  - Deep Learning
AB  - Due to the FMD(foot-and-mouth disease), the domestic animal husbandry and related industries suffer enormous damage every year. Although various academic researches related to FMD are ongoing, engineering studies on the social effects of FMD are very limited. In this study, we propose a systematic methodology to analyze emotional responses of regular citizens on FMD using text mining techniques. The proposed system first collects data related to FMD from the tweets posted on Twitter, and then performs a polarity classification process using a deep-learning technique. Second, keywords are extracted from the tweet using LDA, which is one of the typical techniques of topic modeling, and a keyword network is constructed from the extracted keywords. Finally, we analyze the various social effects of regular citizens on FMD through keyword network. As a case study, we performed the emotional analysis experiment of regular citizens about FMD from July 2010 to December 2011 in Korea.