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基于词条向量和情感分析的新闻广告匹配模型 被引量：1

Matching Model of News and Advertisements Based on Term Vectors and Sentiment Analysis
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摘要 在新闻网页上配置广告的算法通常以信息关联度为基础,没有考虑新闻和广告之间的情感差异,容易导致错误匹配.针对目前广告匹配算法的缺陷,在关键词生成、情感分析技术的基础上,引入情感差异分析,建立了一种新的支持在线新闻与广告内容、情感相匹配的模型.该模型对在线新闻网站和广告投放商实际应用具有重要的意义. Current algorithm of matching advertisements for online news, which is based on information correlation,neglecting sentiment distinctions between news and advertisements, is easy to produce mismatch. Aiming at the defect of matching algorithm, the sentiment distinction analysis was introduced, and a new model to support a better type of matching contents and sentiment between online news and adver-tisements was proposed based on mature techniques of keyword generation and the sentiment analysis. This model has an important significance of practical application for online news websites and advertisers.
作者 李鸿燕
机构地区 上海工程技术大学基础教学学院
出处 《上海工程技术大学学报》 CAS 2011年第4期366-369,共4页 Journal of Shanghai University of Engineering Science
关键词 词条向量 情感分析 概要 绝对位置分数 相对位置分数 情感差异分数 term vector sentiment analysis profile absolute position score relative position score sentiment distinction score
分类号 TP39 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献12

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二级参考文献40

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共引文献92

同被引文献12

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引证文献1

二级引证文献2

上海工程技术大学学报
2011年 第4期

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