DMCA
Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences (2003)
Cached
Download Links
| Venue: | In Proceedings of EMNLP-03 |
| Citations: | 205 - 4 self |
BibTeX
@INPROCEEDINGS{Yu03towardsanswering,
author = {Hong Yu},
title = {Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences},
booktitle = {In Proceedings of EMNLP-03},
year = {2003},
pages = {129--136}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Opinion question answering is a challenging task for natural language processing. In this paper, we discuss a necessary component for an opinion question answering system: separating opinions from fact, at both the document and sentence level. We present a Bayesian classifier for discriminating between documents with a preponderance of opinions such as editorials from regular news stories, and describe three unsupervised, statistical techniques for the significantly harder task of detecting opinions at the sentence level. We also present a first model for classifying opinion sentences as positive or negative in terms of the main perspective being expressed in the opinion. Results from a large collection of news stories and a human evaluation of 400 sentences are reported, indicating that we achieve very high performance in document classification (upwards of 97 % precision and recall), and respectable performance in detecting opinions and classifying them at the sentence level as positive, negative, or neutral (up to 91 % accuracy). 1
Keyphrases
opinion sentence opinion question sentence level separating opinion first model document classification regular news story human evaluation news story challenging task respectable performance bayesian classifier natural language processing high performance large collection main perspective necessary component opinion question answering statistical technique
