A Survey on Opinion Reason Mining and Interpreting Sentiment Variations
Abstract
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.
Description
Keywords
Emerging topic, event detection, interpreting sentiment variations, opinion reason mining,
sentiment analysis, sentiment reasoning, sentiment spikes, topic modeling
Citation
Alattar, F. and Shaalan, K. (2021) “A Survey on Opinion Reason Mining and Interpreting Sentiment Variations,” IEEE Access, 9.