Twitter推文与情感词典SentiWordNet匹配算法研究

被引：2

作者：

易顺明 ^{[1
]}

周洪斌 ^{[1
]}

周国栋 ^{[2
]}

机构：

[1] 沙洲职业工学院电子信息工程系

[2] 苏州大学计算机科学与技术学院

来源：

南京师范大学学报(工程技术版) | 2016年 / 16卷 / 03期

关键词：

推文; 情感分类; SentiWordNet; 匹配算法;

D O I：

暂无

中图分类号：

TP391.1 [文字信息处理];

学科分类号：

摘要：

在Twitter情感分类研究中,经常会采用将推文中的单词匹配情感词典中的同义词条查找相应情感值的方法 .但推文书写比较随意,包含许多俚语、缩写和特殊符号,导致许多词汇与情感词典中的词条无法匹配,匹配率不高直接影响推文的情感分类性能.针对Twitter的语言特征,提出了一套Twitter推文与情感词典SentiWordNet的匹配算法.该算法首先通过对推文内容进行数据清洗、替代处理、词性标注和词形还原等预处理,增加了命名实体识别、对hashtags内容的断词处理、基于Word Clusters的否定句处理和词组匹配等方法 .实验结果表明,采用此方法的匹配率可达90%以上.

引用

页码：41 / 47+53 +53

页数：8

共 20 条

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