Paper: An Iterative Reinforcement Approach for Fine-Grained Opinion Mining

ACL ID N09-1055
Title An Iterative Reinforcement Approach for Fine-Grained Opinion Mining
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors
  • Weifu Du (Harbin Institute of Technology, Harbin China)
  • Songbo Tan (Institute of Computing Technology, Beijing China)

With the in-depth study of sentiment analysis research, finer-grained opinion mining, which aims to detect opinions on different review fea- tures as opposed to the whole review level, has been receiving more and more attention in the sentiment analysis research community re- cently. Most of existing approaches rely mainly on the template extraction to identify the ex- plicit relatedness between product feature and opinion terms, which is insufficient to detect the implicit review features and mine the hid- den sentiment association in reviews, which satisfies (1) the review features are not appear explicit in the review sentences; (2) it can be deduced by the opinion words in its context. From an information theoretic point of view, this paper proposed an iterative reinforcem...