  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. To tackle this issue, this paper introduces two novel
indicators of topic and sentiment for the short and sparse text data. 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.
