A Recommendation Mechanism for Web Publishing Based on Sentiment Analysis of Microblog

Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mechanism are extracted by natural language processing including segmentation, lexical...

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Bibliographic Details
Published inWuhan University journal of natural sciences Vol. 20; no. 2; pp. 146 - 152
Main Authors Tian, Pingfang, Zhu, Zhonghua, Xiong, Li, Xu, Fangfang
Format Journal Article
LanguageEnglish
Published Wuhan Wuhan University 01.04.2015
Subjects
Biomedical and Life Sciences
Communities
Computer Science
Data mining
Knowledge bases (artificial intelligence)
Life Sciences
Materials Science
Natural language processing
Platforms
Query processing
Recommender systems
Semantics
Strategy
Web发布
情感
机制
海量数据
用户群体
自然语言处理
词法分析
语义查询
TP 393
microblog
background knowledge base
sentiment analysis
keyword extraction
linked open data
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Summary:Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mechanism are extracted by natural language processing including segmentation, lexical analysis and strategy selection. Then, we query the background knowledge base based on linked open data （LOD） with the basic information of users. The experiment result shows that the accuracy of recommendation is within the range of 70% -89% with sentiment analysis and semantic query. Compared with traditional recommendation method, this method can satisfy users＇ requirement greatly.
Bibliography:Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mechanism are extracted by natural language processing including segmentation, lexical analysis and strategy selection. Then, we query the background knowledge base based on linked open data （LOD） with the basic information of users. The experiment result shows that the accuracy of recommendation is within the range of 70% -89% with sentiment analysis and semantic query. Compared with traditional recommendation method, this method can satisfy users＇ requirement greatly.
42-1405/N
sentiment analysis; microblog; keyword extraction;linked open data; background knowledge base
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1007-1202
1993-4998
DOI:10.1007/s11859-015-1073-1