document: Mining the peanut gallery : opinion extraction and semantic classification of product reviews 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 .
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