Measuring praise and criticism: Inference of semantic orientation from association
Measuring praise and criticism: Inference of semantic orientation from association
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DOI：
10.1145/944012.944013
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发表时间：
2003-10-01
影响因子：
5.6
通讯作者：
Littman, ML
中科院分区：
文献类型：
--
作者：
Turney, PD;Littman, ML
一个词的评价性特征被称为它的语义指向。积极的语义取向表示赞扬（例如，“诚实”，“无畏”）和否定的语义取向表示批评（例如，“令人不安的”，“多余的”）。语义取向在两个方向（积极或消极）和程度（轻度到强烈）上都有变化。用于测量语义取向的自动化系统将应用于文本分类、文本过滤、跟踪在线讨论中的意见、分析调查响应和自动聊天系统（聊天机器人）。本文介绍了一种从一个词与一组正范式词和负范式词的统计关联来推断该词的语义指向的方法。这种方法的两个实例进行评估，基于两种不同的统计措施的词关联：逐点互信息（PMI）和潜在语义分析（LSA）。该方法用3，596个单词（包括形容词，副词，名词和动词）进行了实验测试，这些单词已被手动标记为阳性（1，614个单词）和阴性（1，982个单词）。该方法达到了82.8%的准确率在全测试集上，但准确率上升到95%以上时，该算法被允许放弃从分类温和的话。
The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", " superfluous"). Semantic orientation varies in both direction ( positive or negative) and degree ( mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words ( including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive ( 1,614 words) and negative ( 1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words.