Before revision:  Recently, the research on text-based crude oil price forecasting has advanced. To improve accuracy, some studies have added as many covariates as possible, such as textual and nontextual factors, to their models, leading to unnecessary human intervention and computational costs. Moreover, some methods are only designed for crude oil forecasting and cannot be well transferred to the forecasting of other similar futures commodities. In contrast, this article proposes a text-based forecasting framework for futures commodities that uses only future news headlinesobtained from Investing.com to forecast crude oil prices. Two marketing indexes, the sentiment index and the topic intensity index, are extracted from these news headlines . Considering that the public's sentiment changes over time, the time factor is innovatively applied to the construction of the sentiment index.
After revision:  Recently, the research on text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and the topic intensity index, are extracted from these news headlines . Considering that the public's sentiment changes over time, the time factor is innovatively applied to the construction of the sentiment index.