Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank
13988Authors
nan
Abstract
AbstractWe present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification accuracies across multiple domains for the base models and the maximum entropy model bootstrapped from the PageRank annotation.
Keywords
Full Content
nan
Labeled Text
AbstractWe present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification accuracies across multiple domains for the base models and the maximum entropy model bootstrapped from the PageRank annotation.
Publication Details
- Paper ID:
- 13988
- Year:
- 2012
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan