计算机科学
赞扬
自然语言处理
人工智能
方向(向量空间)
点态互信息
名词
集合(抽象数据类型)
词(群论)
潜在语义分析
批评
语义学(计算机科学)
语言学
心理学
相互信息
数学
文学类
哲学
艺术
程序设计语言
心理治疗师
几何学
作者
Peter D. Turney,Michael L. Littman
标识
DOI:10.1145/944012.944013
摘要
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.
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