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Paper Information

lrec2012-main-012

Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank

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Title

Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank

Abstract

We 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.


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