Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
<|reference_start|>Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews: 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.<|reference_end|>
@article{turney2002thumbs,
    title={Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised
  Classification of Reviews},
    author={Peter D. Turney (National Research Council of Canada)},
    journal={Proceedings of the 40th Annual Meeting of the Association for
  Computational Linguistics, (2002), Philadelphia, Pennsylvania, 417-424},
    year={2002},
    number={NRC-44946},
    archivePrefix={arXiv},
    eprint={cs/0212032},
    primaryClass={cs.LG cs.CL cs.IR}
}