Dave et. al., WWW 2003

This is a summary of research paper as part of Social Media Analysis 10-802, Fall 2012.

Citation

Dave, K., Lawrence, S., and Pennock, D.M. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. WWW 2003.

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Abstract from the paper

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.

Summary

Overview

This paper proposes some techniques for opinion mining and classification of opinions as positive or negative. It discusses various contemporary methods used for sentiment classification and how they cater to different tasks.

Proposed Techniques

The system trains a classifier using self-tagged product reviews from websites such as amazon.com and c|net.com

Study Plan

Resources useful for understanding this paper

• Article: Opinion Mining