[1]赵文清,侯小可,沙海虹.语义规则在微博热点话题情感分析中的应用[J].智能系统学报,2014,9(01):121-125.[doi:10.3969/j.issn.1673-4785.201208020]
 ZHAO Wenqing,HOU Xiaoke,SHA Haihong.Application of semantic rules to sentiment analysis of microblog hot topics[J].CAAI Transactions on Intelligent Systems,2014,9(01):121-125.[doi:10.3969/j.issn.1673-4785.201208020]
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语义规则在微博热点话题情感分析中的应用(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第9卷
期数:
2014年01期
页码:
121-125
栏目:
出版日期:
2014-02-25

文章信息/Info

Title:
Application of semantic rules to sentiment analysis of microblog hot topics
作者:
赵文清1 侯小可1 沙海虹2
1. 华北电力大学(保定) 控制与计算机工程学院, 河北 保定 071003;
2. 英业达集团(北京)电子技术有限公司 开发部, 北京 100086
Author(s):
ZHAO Wenqing1 HOU Xiaoke1 SHA Haihong2
1. School of Control and Computer Engineering, North China Electric Power University(Baoding), Baoding 071003, China;
2. Inventec(Beijing) Electronics Technology Co., Ltd., Beijing 100086, China
关键词:
微博热点话题情感分析语义规则情感词典
Keywords:
microbloghot topicssentiment analysissemantic rulesemotional dictionary
分类号:
TP391.1
DOI:
10.3969/j.issn.1673-4785.201208020
摘要:
近来, 针对微博热点话题的情感分析研究得到了广泛关注, 而基于监督的学习方法在分析文本时会忽视词语的上下文联系。根据中文微博的特点, 提出了一种基于语义规则的方法对微博热点话题进行情感分析。该方法首先需要人工整理出程度副词表、否定词表和微博中默认表情符号的褒贬分类。然后在情感词语计算的基础上, 考虑上下文中否定词和程度词对修饰情感词语的情感倾向和情感强度的影响, 同时也设定规则计算表情符号对一条微博的情感倾向判断的作用。最后与基于情感词典的方法做实验对比, 实验结果表明该方法在文本情感倾向性识别的准确率上有了一定提高。
Abstract:
The research on the sentiment analysis for microblog hot topics has attracted much attention recently, while the studying method on the basis of supervision neglects the context of a word in the analysis of text. According to the characteristics of Chinese microblogs, a method based on semantic rules is proposed for sentiment analysis of microblog hot topics. As for the method, firstly, we need to manually sort out a degree adverb list, a negative word list and the appraisal category of the expression symbols defaulted in a microblog. Secondly, on the basis of the calculation of sentiment words, we consider the impact of negative words and degree words in the context of the emotional tendency and strength decorating sentiment words; in addition, we also set rules for calculating the influence of the expression symbol on the sentiment tendency judgment of a piece of microblog. Finally, our proposed method is compared with the method based on the emotional dictionary. The experimental results show that the proposed method improves the identification accuracy of the text sentiment tendency.

参考文献/References:

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相似文献/References:

[1]赵文清,侯小可.基于词共现图的中文微博新闻话题识别[J].智能系统学报,2012,7(05):444.
 ZHAO Wenqing,HOU Xiaoke.News topic recognition of Chinese microblog based on word cooccurrence graph[J].CAAI Transactions on Intelligent Systems,2012,7(01):444.
[2]韩忠明,张慧,张梦.基于内容的热点话题传播模型[J].智能系统学报,2013,8(03):233.
 HAN Zhongming,ZHANG Hui,ZHANG Meng.A hot topic propagation model based on topic contents[J].CAAI Transactions on Intelligent Systems,2013,8(01):233.
[3]刘志雄,贾彩燕.面向用户兴趣与社区关系的微博话题检测方法[J].智能系统学报,2016,11(3):294.[doi:10.11992/tis.201603341]
 LIU Zhixiong,JIA Caiyan.Micro-blog topic detection based on users’ interests and communities[J].CAAI Transactions on Intelligent Systems,2016,11(01):294.[doi:10.11992/tis.201603341]

备注/Memo

备注/Memo:
收稿日期:2012-08-14。
基金项目:国家自然科学基金资助项目(70671039);中央高校基本科研业务费专项资金资助项目(12MS121).
作者简介:侯小可,男,1985年生,硕士研究生,主要研究方向为人工智能、数据挖掘等;沙海虹,男,1971年生,高级工程师,主要研究方向为人工智能、数据挖掘。
通讯作者:赵文清,女,1973年生,副教授,中国人工智能学会粗糙集与软计算专业委员会委员,主要研究方向为机器学习、数据挖掘、贝叶斯网络学习等。获河北省科技进步三等奖1项、国家发明专利1项,发表学术论文30余篇,出版教材3部.E-mail:houxiaoke2008@163.com.
更新日期/Last Update: 1900-01-01