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A supernetwork-based online post informative quality evaluation model.

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Title: A supernetwork-based online post informative quality evaluation model.
Authors: Chi, Yuxue1,2 chiyuxue15@mails.ucas.ac.cn, Tang, Xianyi3 tangxianyi@tongji.edu.cn, Lian, Ying1,2 lianying15@mails.ucas.ac.cn, Dong, Xuefan1,2 dongxuefan15@mails.ucas.ac.cn, Liu, Yijun1,2 yijunliu@casipm.ac.cn
Source: Knowledge-Based Systems. Mar2019, Vol. 168, p10-24. 15p.
Subjects: Big data, PostScript (Computer program language), Public opinion, Machine learning, Criticism
Abstract: Abstract In an era of big data, explosive growth of online posts makes the judgment of their qualities harder while more important. In many cases, people want to quickly identify those most informative posts, which contain details, insights or in-depth criticisms, to help them make better decisions. To meet this demand, a three-stage model is proposed. First, a super-network model is introduced to accommodate the multidimensional attributes of online posts, including keywords, user ID, emotions and the related event. Second, a corpus updating mechanism is introduced to generate event specific corpora, which help to discriminate the informative quality of posts in the next stage. Third, machine learning algorithms are applied, where the posts are first filtered by a linear discriminant classifier and then assessed by a multilayer perceptron neural network. To test the model, we chose six online public opinion events that fell into two categories: major public safety crisis and online controversies about public policies. Experimental results showed the effectiveness of the proposed model, where majority of errors are less than 0.05, on a 0–1 measuring scale. In the future, this model may also be adapted to areas including evaluation of informative quality of websites, product reviews and answers in question and answer communities. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Abstract In an era of big data, explosive growth of online posts makes the judgment of their qualities harder while more important. In many cases, people want to quickly identify those most informative posts, which contain details, insights or in-depth criticisms, to help them make better decisions. To meet this demand, a three-stage model is proposed. First, a super-network model is introduced to accommodate the multidimensional attributes of online posts, including keywords, user ID, emotions and the related event. Second, a corpus updating mechanism is introduced to generate event specific corpora, which help to discriminate the informative quality of posts in the next stage. Third, machine learning algorithms are applied, where the posts are first filtered by a linear discriminant classifier and then assessed by a multilayer perceptron neural network. To test the model, we chose six online public opinion events that fell into two categories: major public safety crisis and online controversies about public policies. Experimental results showed the effectiveness of the proposed model, where majority of errors are less than 0.05, on a 0–1 measuring scale. In the future, this model may also be adapted to areas including evaluation of informative quality of websites, product reviews and answers in question and answer communities. [ABSTRACT FROM AUTHOR]
ISSN:09507051
DOI:10.1016/j.knosys.2018.12.027