Paper Info
Title
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
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.
Year
DOI
Venue
2003
10.1145/775152.775226
WWW
Keywords
Field
DocType
aggregating opinion,feature extraction,product review,simple method,opinion extraction,complete web-based tool,semantic classification,best method,peanut gallery,daunting task,opinion mining tool,product attribute,web search,algorithms,machine learning,information retrieval,opinion mining,measurement
Data mining,Computer science,Heuristics,Artificial intelligence,Classifier (linguistics),Ambiguity,Opinion extraction,Document classification,World Wide Web,Information retrieval,Sentiment analysis,Feature extraction,Product reviews,Machine learning
Conference
ISBN
Citations 
PageRank 
1-58113-680-3
793
79.12
References 
Authors
18
3
Search Limit
100793
Name
Order
Citations
PageRank
Kushal Dave11245137.90
Steve Lawrence26194872.30
David M. Pennock33823451.85