Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and the topic model and further affecting the forecasting performance. Exploiting advanced and appropriate methods to construct high-quality features from news headlines becomes crucial in crude oil forecasting. This paper introduces two novel indicators of topic and sentiment for the short and sparse text data to tackle this issue. Empirical experiments show that AdaBoost.RT with our proposed text indicators, with a more comprehensive view and characterization of the short and sparse text data, outperforms the other benchmarks. Another significant merit is that our method also yields good forecasting performance when applied to other futures commodities.

稀疏和简短的新闻标题可能是任意的、嘈杂的和模棱两可的，这使得设计用于容纳长文本的经典主题模型LDA（潜在狄利克雷分配）很难从中发现知识。尽管如此，现有的一些基于文本的原油预测研究采用LDA从新闻标题中挖掘主题，导致短文本与主题模型不匹配，进一步影响预测性能。利用先进和适当的方法从新闻标题中构建高质量的特征在原油预测中变得至关重要。本文针对短文本数据和稀疏文本数据引入了两个新的主题和情感指标来解决这个问题。实证实验表明AdaBoost.RT使用我们提出的文本指标，对短和稀疏文本数据有更全面的看法和特征，优于其他基准。另一个重要的优点是我们的方法在应用于其他期货商品时也能产生良好的预测性能。