基于HGSD的消费情感分类算法  

The HGSD Method for Consumption Sentiment Classification

作　　者：林明明[1] 邱云飞[1] 邵良杉[2] 

机构地区：[1]辽宁工程技术大学软件学院,葫芦岛125100 [2]辽宁工程技术大学系统工程研究所,葫芦岛125100

出　　处：《模式识别与人工智能》2015年第6期490-498,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.70971059);辽宁省创新团队项目(No.2009T045);辽宁省高等学校杰出青年学者成长计划项目(No.LJQ2012027)资助

摘　　要：针对中文消费评价的情感分类问题,构造基于词典语义概念和上下文语义相结合的情感分类方法,对情感进行分类.该方法首先构造提取不同领域基准词集的方法.然后利用一元语言模型,通过How Net计算情感相似值,进行情感词提取.最后结合How Net方法和Google相似距离方法构造一种情感分类算法,对句子进行情感倾向性分类,既考虑词语本身含义,又考虑词语在上下文中的含义.通过对书籍、电脑和酒店的评价进行实验,F值较高,同时与其他方法进行对比实验,体现文中算法的有效性.Aiming at the sentiment classification for Chinese consumption comments, a sentiment classification method combining dictionary semantic concept and context semanteme is proposed. Firstly, a method of extracting benchmark words set of different domains is put forword. Then, the sentiment words are extracted through the similarity of HowNet based on the unigram model. Finally, HowNet and Google similarity distance （ HGSD ） combining the HowNet similarity and the Google similarity distance is presented to classify the sentences, which reflects the original meaning of the word and the meaning in the context. Experiments of consumption comments on books, computers and hotels show the higher F-measure of the proposed method, and meanwhile the contrast experiment shows the effectiveness of the proposed algorithm.

关 键 词：词典语义 上下文语义 情感分类 知网 Google相似距离 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]