Conference Proceeding Article
With the growing popularity of opinion-rich resources on the Web, new opportunities and challenges arise and aid people in actively using such information to understand the opinions of others. Opinion mining process currently focuses on extracting the sentiments of the users on products, social, political and economical issues. In many instances, users not only express their sentiments but also contribute their ideas, requests and suggestions through comments. Such comments are useful for domain experts and are referred to as actionable content. Extracting actionable knowledge from online social media has attracted a growing interest from both academia and the industry. We define a new problem in this line which is extracting entity-actionable knowledge from the users’ comments. The problem aims at extracting and normalizing the entity-action pairs. We propose a principled approach to solve this problem and detect exactly matched entities with 75.1% F-score and exactly matched actions with 76.43% F-score. We could achieve an average precision of 81.15% for entity-action normalization.
Information Extraction, Normalization, Clustering, Conditional Random Fields
Communication Technology and New Media | Databases and Information Systems
Data Management and Analytics
Proceedings of COLING 2012, December 8-15, Mumbai
City or Country
GOTTIPATI, Swapna and JIANG, Jing.
Extracting and Normalizing Entity-actions from Users' Comments. (2012). Proceedings of COLING 2012, December 8-15, Mumbai. 995-1010. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1706
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.