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A:
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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C:
Turney, P.D. (2002). Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. ACL.
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K:
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P:
https://aclanthology.org/P02-1053/
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D:
https://aclanthology.org/P02-1053.pdf
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G:
https://app.razzi.my/findgref?gid=1MSx8ggcFJ9yOAVIw7WAXkIWSUlsIMapm
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