拇指朝上还是拇指朝下？语义定向在评论无监督分类中的应用
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
摘要：
本文提出了一种简单的无监督学习算法，用于将评论分类为推荐（竖起大拇指）或不推荐（竖起大拇指）。评论的分类是通过评论中包含形容词或副词的短语的平均语义取向来预测的。当一个短语具有良好的关联时，它具有积极的语义取向（例如，“微妙的细微差别”）以及当它具有不良关联时的负面语义取向（例如，“非常骑士”）。本文将短语的语义指向计算为给定短语与词“优”之间的互信息减去给定短语与词“差”之间的互信息。如果一个评论的短语的平均语义取向是积极的，则该评论被归类为推荐。该算法在Epperly的410条评论上进行评估时，平均准确率为74%，这些评论来自四个不同的领域（汽车，银行，电影和旅游目的地）。准确率从汽车评论的84%到电影评论的66%不等。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
