Predicting Sales Performance Based on Polarity Sentiments of Online Reviews and Manifold Dynamics Method

Zixin Dou, Yongjun Hu, Peng Cheng, Lijuan Huang, Jiuzhen Liang, Hailian Xiao
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引用次数: 2

Abstract

Online reviews provide consumers with information about products and services that may affect their purchasing decisions. As such, the customer attitude in the reviews play a important role to product sales. In this study, new sentiment prediction method is presented to enhance the forecasting accuracy by utilizing review dual-sentiments and employing non- linear manifold dynamics algorithm. Not only this method can extract and separate the polarity sentiment factors from the content of each online review, but also can remove the seasonality of products and mix the different types’ data. Extensive experimental results validate the effectiveness of our proposed method.
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基于在线评论极性情绪和流形动力学方法的销售业绩预测
在线评论为消费者提供了有关产品和服务的信息,这些信息可能会影响他们的购买决定。因此,顾客在评论中的态度对产品的销售起着重要的作用。本文提出了一种新的情感预测方法,利用回顾双情感和非线性流形动力学算法来提高预测精度。该方法不仅可以从每条在线评论的内容中提取和分离极性情感因素,而且可以去除产品的季节性,混合不同类型的数据。大量的实验结果验证了该方法的有效性。
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