Anomaly detection through enhanced sentiment analysis on social media data

• Main Authors: WANG, Zhaoxia, JOO, Victor, TONG, Chuan, XIN, Xin, CHIN, Hoong Chor
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Sentiment classification; Social media; Twitter; Anomaly detection; Enhanced sentiment analysis; Machine-learning; Pattern classification; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/5567; https://ink.library.smu.edu.sg/context/sis_research/article/6570/viewcontent/AnomalyDetection_2014_av.pdf
• Institution: Singapore Management University

Anomaly detection in sentiment analysis refers to detecting abnormal opinions, sentiment patterns or special temporal aspects of such patterns in a collection of data. The anomalies detected may be due to sudden sentiment changes hidden in large amounts of text. If these anomalies are undetected or...