Extending sparse text with induced domain-specific lexicons and embeddings: A case study on predicting donations.

This paper addresses the problem of expanding sparse textual content to increase the accuracy of data-driven prediction tasks. We evaluate the use of word embeddings and lexicons within the context of a donation prediction task, where we classify potential donors as either likely or unlikely to donate. We perform several comparative experiments and analyses, and show that our methods to automatically enhance sparse textual data significantly improve the predictive performance on this task.