An evolution algorithm of opinion tendency in Internet

Yun Liu; Lei Deng; Michael R. Bartolacci; Qing An Zeng

doi:10.3966/199115592016102703006

An evolution algorithm of opinion tendency in Internet

Yun Liu, Lei Deng, Michael R. Bartolacci, Qing An Zeng

Division of Engineering, Business & Computing (Berks)

Research output: Contribution to journal › Article › peer-review

Abstract

We empirically analyze a "hot topic" of online marketing to exam the predictive performance of an opinion evolution model. We compare our model with existing algorithms of online prediction and conduct an analysis using a test data set. Results show that our model and corresponding information influence rule have increased prediction performance.

Original language	English (US)
Pages (from-to)	53-61
Number of pages	9
Journal	Journal of Computers (Taiwan)
Volume	27
Issue number	3
DOIs	https://doi.org/10.3966/199115592016102703006
State	Published - 2016

All Science Journal Classification (ASJC) codes

Computer Science(all)

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10.3966/199115592016102703006

Cite this

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title = "An evolution algorithm of opinion tendency in Internet",

abstract = "We empirically analyze a {"}hot topic{"} of online marketing to exam the predictive performance of an opinion evolution model. We compare our model with existing algorithms of online prediction and conduct an analysis using a test data set. Results show that our model and corresponding information influence rule have increased prediction performance.",

author = "Yun Liu and Lei Deng and Bartolacci, {Michael R.} and Zeng, {Qing An}",

note = "Funding Information: This research is supported by National Natural Science Foundation under Grant 61371071, Beijing Natural Science Foundation under Grant 4132057, Beijing Science and Technology Program under Grant Z121100007612003, Academic Discipline and Postgraduate Education Project of Beijing Municipal Commission of Education.",

year = "2016",

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AU - Liu, Yun

AU - Deng, Lei

AU - Bartolacci, Michael R.

AU - Zeng, Qing An

N1 - Funding Information: This research is supported by National Natural Science Foundation under Grant 61371071, Beijing Natural Science Foundation under Grant 4132057, Beijing Science and Technology Program under Grant Z121100007612003, Academic Discipline and Postgraduate Education Project of Beijing Municipal Commission of Education.

PY - 2016

Y1 - 2016

N2 - We empirically analyze a "hot topic" of online marketing to exam the predictive performance of an opinion evolution model. We compare our model with existing algorithms of online prediction and conduct an analysis using a test data set. Results show that our model and corresponding information influence rule have increased prediction performance.

AB - We empirically analyze a "hot topic" of online marketing to exam the predictive performance of an opinion evolution model. We compare our model with existing algorithms of online prediction and conduct an analysis using a test data set. Results show that our model and corresponding information influence rule have increased prediction performance.

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An evolution algorithm of opinion tendency in Internet

Abstract

All Science Journal Classification (ASJC) codes

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