Paper: Identifying Sentiment Words Using an Optimization-based Model without Seed Words

ACL ID P13-2148
Title Identifying Sentiment Words Using an Optimization-based Model without Seed Words
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

Sentiment Word Identification (SWI) is a basic technique in many sentiment analy- sis applications. Most existing research- es exploit seed words, and lead to low ro- bustness. In this paper, we propose a novel optimization-based model for SWI. Unlike previous approaches, our model exploits the sentiment labels of documents instead of seed words. Several experiments on re- al datasets show that WEED is effective and outperforms the state-of-the-art meth- ods with seed words.