Outlet Title
JOURNAL OF BUSINESS ANALYTICS
Document Type
Article
Publication Date
2018
Abstract
Massive social media data present businesses with an immense opportunity to extract useful insights. However, social media messages typically consist of both facts and opinions, posing a challenge to analytics applications that focus more on either facts and opinions. Distinguishing facts and opinions may significantly improve subsequent analytics tasks. In this study, we propose a deep learning-based algorithm that automatically separates facts from opinions in Twitter messages. The algorithm outperformed multiple popular baselines in an experiment we conducted. We further applied the proposed algorithm to track customer complaints and found that it indeed benefits subsequent analytics applications.
Recommended Citation
Shan, Ronghua; Chatterjee, Swayambhu P.; Deng, Shuyuan; Liu, Jun; and Jiao, Wu, "Classifying facts and opinions in Twitter messages: a deep learning-basedapproach" (2018). Faculty Research & Publications. 344.
https://scholar.dsu.edu/bispapers/344