The Impact Factors of Online Book Reviews Usefulness: An Empirical Comparison Between ANN and SVM

机译：在线书评有用性的影响因素：ANN和SVM的经验比较

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The rapid development of information technology has led to massive online reviews which are generated on the Web, this paper aims to explore the determinants of review helpfulness. In this research, we propose a conceptual model from the perspective of content quality and source quality and incorporate supervised machine learning approaches to learn the impact factors of online book reviews. Specifically, we compared two supervised machine learning algorithms of ANN (Artificial Neural Network) and SVM (Support Vector Machine) and Linear Regression approach. Book reviews are collected from douban.com for ten popular books. We found that long sentences and the extreme emotion is more helpful and can be more persuasive. The empirical findings also indicated that the ANN and SVM algorithms outperformed the ordinary OLS algorithms.

机译：信息技术的飞速发展导致大量在线评论在Web上生成，本文旨在探讨评论有用性的决定因素。在这项研究中，我们从内容质量和源质量的角度提出了一个概念模型，并结合了受监督的机器学习方法来学习在线书评的影响因素。具体来说，我们比较了ANN（人工神经网络）和SVM（支持向量机）和线性回归方法的两种监督式机器学习算法。从douban.com收集了十本热门书籍的书评。我们发现，冗长的句子和极端的情感会更有帮助，并且更有说服力。实验结果还表明，人工神经网络和支持向量机算法优于普通的OLS算法。

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《International Conference on Service Systems and Service Management》|2018年|1-6|共6页
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An Han; Ren Jifan; Zhu Mengyang;
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Support vector machines; Book reviews; Linear regression; Machine learning; Input variables; Predictive models; Kernel;

机译：支持向量机;书评;线性回归;机器学习;输入变量;预测模型;内核;

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The Impact Factors of Online Book Reviews Usefulness: An Empirical Comparison Between ANN and SVM

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