A Survey on Opinion Reason Mining and Interpreting Sentiment Variations

• Published in: IEEE access Vol. 9; pp. 39636 - 39655
• Main Authors: Alattar, Fuad, Shaalan, Khaled
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blogs; Cognition; Cognition & reasoning; Data mining; Decision making; Digital media; Emerging topic; Empirical analysis; event detection; Feature extraction; Hidden Markov models; interpreting sentiment variations; opinion reason mining; Reasoning; Sentiment analysis; sentiment reasoning; sentiment spikes; Social networking (online); Task analysis; topic modeling
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3063921

Tracking social media sentiment on a desired target is certainly an important query for many decision-makers in fields like services, politics, entertainment, manufacturing, etc. As a result, there has been a lot of focus on Sentiment Analysis. Moreover, some studies took one step ahead by analyzing subjective texts further to understand possible motives behind extracted sentiments. Few other studies took several steps ahead by attempting to automatically interpret sentiment variations. Learning reasons from sentiment variations is indeed valuable, to either take necessary actions in a timely manner or learn lessons from archived data. However, machines are still immature to carry out the full Sentiment Variations' Reasoning task perfectly due to various technical hurdles. This paper attempts to explore main approaches to Opinion Reason Mining, with focus on Interpreting Sentiment Variations. Our objectives are investigating various methods for solving the Sentiment Variations' Reasoning problem and identifying some empirical research gaps. To identify these gaps, a real-life Twitter dataset is analyzed, and key hypothesis for interpreting public sentiment variations are examined.