Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes
Marina Sedinkina, Nikolas Breitkopf, Hinrich Schütze · Open access LMU (Ludwid Maxmilian's Universitat Munchen) · 2019
In this paper, we automatically create senti- ment dictionaries for predicting financial out- comes. We compare three approaches: (i) manual adaptation of the domain-general dic- tionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first man- ual, then automatic adaptation. In our experi- ments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the finan- cial outcomes excess return and volatility. In particular, automatic adaptation performs bet- ter than manual adaptation. In our analysis, we find that annotation based on an expert’s a priori belief about a word’s meaning can be incorrect – annotation should be performed based on the word’s contexts in the target do- main instead.