Do Intelligent Robots Need Emotion?

What's your opinion?

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


 

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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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S:

https://www.semanticscholar.org/paper/Thumbs-Up-or-Thumbs-Down-Semantic-Orientation-to-of-Turney/9e7c7853a16a378cc24a082153b282257a9675b7

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R:

https://www.researchgate.net/publication/2537987_Thumbs_Up_or_Thumbs_Down_Semantic_Orientation_Applied_to

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G:

https://app.razzi.my/findgref?gid=1MSx8ggcFJ9yOAVIw7WAXkIWSUlsIMapm

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