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Aggregation Methods for Proximity-Based Opinion Retrieval
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AI
DOI：10.1145/2382438.2382445.png)
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摘要
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The enormous amount of user-generated data available on the Web provides a great opportunity to understand, analyze, and exploit people's opinions on different topics. Traditional Information Retrieval methods consider the relevance of documents to a topic but are unable to differentiate between subjective and objective documents. Opinion retrieval is a retrieval task in which not only the relevance of a document to the topic is important but also the amount of opinion expressed in the document about the topic. In this article, we address the blog post opinion retrieval task and propose methods that rank blog posts according to their relevance and opinionatedness toward a topic. We propose estimating the opinion density at each position in a document using a general opinion lexicon and kernel density functions. We propose and investigate different models for aggregating the opinion density at query terms positions to estimate the opinion score of every document. We then combine the opinion score with the relevance score based on a probabilistic justification. Experimental results on the BLOG06 dataset show that the proposed method provides significant improvement over the standard TREC baselines. The proposed models also achieve much higher performance compared to all state of the art methods.
Keyword:
Experimentation
Performance
Opinion
sentiment
blog
retrieval
proximity
期刊
IF:
9.1
论文数:
1.2K
被引数:
4.7K
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