Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

TL;DR

This paper introduces an unsupervised method for classifying reviews as positive or negative based on the average semantic orientation of phrases, achieving up to 84% accuracy across various domains.

Contribution

It presents a novel unsupervised approach using mutual information to determine semantic orientation for review classification.

Findings

Achieves 74% average accuracy on 410 reviews

Accuracy varies from 66% to 84% across domains

Uses mutual information with 'excellent' and 'poor' for orientation

Abstract

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.