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Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews (2003)
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BibTeX
@INPROCEEDINGS{Dave03miningthe,
author = {Kushal Dave and Steve Lawrence and David M. Pennock},
title = {Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews},
booktitle = {},
year = {2003},
pages = {519--528}
}
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Abstract
The web contains a wealth of product reviews, but sifting through them is a daunting task. Ideally, an opinion mining tool would process a set of search results for a given item, generating a list of product attributes (quality, features, etc.) and aggregating opinions about each of them (poor, mixed, good). We begin by identifying the unique properties of this problem and develop a method for automatically distinguishing between positive and negative reviews. Our classifier draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation. The best methods work as well as or better than traditional machine learning. When operating on individual sentences collected from web searches, performance is limited due to noise and ambiguity. But in the context of a complete web-based tool and aided by a simple method for grouping sentences into attributes, the results are qualitatively quite useful.
Keyphrases
product review opinion extraction semantic classification peanut gallery information retrieval technique complete web-based tool individual sentence web search various metric opinion mining tool simple method feature extraction negative review traditional machine learning method work search result unique property product attribute
