Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes

Marina Sedinkina, Nikolas Breitkopf, Hinrich Schütze


Abstract
In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (i) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better 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 domain instead.
Anthology ID:
P19-1034
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
346–359
Language:
URL:
https://aclanthology.org/P19-1034
DOI:
10.18653/v1/P19-1034
Bibkey:
Cite (ACL):
Marina Sedinkina, Nikolas Breitkopf, and Hinrich Schütze. 2019. Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 346–359, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes (Sedinkina et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1034.pdf