This study proposes a novel data-driven crude oil price prediction methodology using Google Trends and online media text mining. Convolutional neural network (CNN) is used to automatically extract text features from online crude oil news to illustrate the explanatory power of text features for crude oil price prediction. Specifically, our findings contribute to the methodological and theoretical insights for information processing, in that variational mode decomposition is used to construct useful time series indicators based on the outputs of CNN. Experimental results imply that the proposed text-based and online-big-data-based forecasting methods outperform other techniques. A total of 4837 and 3883 news headlines” are collected in two cases, respectively. The mean absolute percentage error of the proposed model is 0.0571 and 0.0459 for crude oil price forecasting of two cases, respectively. Therefore, the complementary relationship between news headlines and Google Trends is beneficial in conducting considerably accurate crude oil price forecasting.

这项研究提出了一种使用Google趋势和在线媒体文本挖掘的新颖的数据驱动的原油价格预测方法。卷积神经网络（CNN）用于自动从在线原油新闻中提取文本特征，以说明文本特征对原油价格预测的解释力。具体而言，我们的发现为信息处理提供了方法论和理论上的见识，因为基于CNN的输出，使用变分模式分解来构建有用的时间序列指标。实验结果表明，所提出的基于文本和基于在线大数据的预测方法优于其他技术。在两种情况下，分别收集了总共4837和3883个新闻头条。所提出模型的平均绝对百分比误差为0.0571和0。0459分别为原油价格预测的两种情况。因此，新闻头条与Google趋势之间的互补关系有利于进行准确的原油价格预测。