2003 MiningThePeanutGallery

(Dave et al., 2003) ⇒ Kushal Dave, Steve Lawrence, and David M. Pennock. (2003). “Mining the Peanut Gallery: Opinion extraction and semantic classification of product reviews.” In: Proceedings of the 12th International Conference on World Wide Web (WWW 2003). doi:10.1145/775152.775226

Subject Headings: Sentiment Analysis Task, Sentiment Analysis Algorithm.

Notes

Cited By

2008

(Pang & Lee, 2008) ⇒ Bo Pang, and Lillian Lee. (2008). “Opinion Mining and Sentiment Analysis.” Now Publishers Inc. doi:10.1561/1500000011

Quotes

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.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 MiningThePeanutGallery	Steve Lawrence David M. Pennock Kushal Dave			Mining the Peanut Gallery: Opinion extraction and semantic classification of product reviews		Proceedings of the 12th International Conference on World Wide Web	http://www.kushaldave.com/p451-dave.pdf	10.1145/775152.775226		2003