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Open Domain Targeted Sentiment
| Citations: | 7 - 0 self |
BibTeX
@MISC{Mitchell_opendomain,
author = {Margaret Mitchell and Jacqueline Aguilar and Theresa Wilson and Benjamin Van Durme},
title = {Open Domain Targeted Sentiment},
year = {}
}
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Abstract
We propose a novel approach to sentiment analysis for a low resource setting. The intuition behind this work is that sentiment expressed towards an entity, targeted sentiment, may be viewed as a span of sentiment expressed across the entity. This representation allows us to model sentiment detection as a sequence tagging problem, jointly discovering people and organizations along with whether there is sentiment directed towards them. We compare performance in both Spanish and English on microblog data, using only a sentiment lexicon as an external resource. By leveraging linguisticallyinformed features within conditional random fields (CRFs) trained to minimize empirical risk, our best models in Spanish significantly outperform a strong baseline, and reach around 90 % accuracy on the combined task of named entity recognition and sentiment prediction. Our models in English, trained on a much smaller dataset, are not yet statistically significant against their baselines. 1
Keyphrases
open domain targeted sentiment sentiment detection microblog data external resource sequence tagging problem linguisticallyinformed feature low resource setting sentiment lexicon empirical risk novel approach entity recognition combined task sentiment prediction strong baseline targeted sentiment conditional random field
