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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.
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https://dl.acm.org/doi/10.3115/1073083.1073153 in https://dl.acm.org/doi/proceedings/10.5555/1073083
https://aclanthology.org/P02-1053/
https://aclanthology.org/P02-1053.pdf
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