@article{MD0CE2218,
title = "Sentiment Analysis of Foot-and-Mouth Disease Using Tweet Text-Mining Technique",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2018",
issn = "2287-5905",
doi = "10.3745/KTSDE.2018.7.11.419",
author = "Heechan Chae, Jonguk Lee, Yoona Choi, Daihee Park, Yongwha Chung",
keywords = "Text Mining, sentiment analysis, FMD, Twitter, Deep Learning",
abstract = "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."
}