Abstract
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.
Item Type: | Conference |
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EU Funded Grant Agreement Number: | 740516 |
EU Projects: | Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement |
Research Centers: | Center for Information and Language Processing (CIS) |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems 400 Language > 410 Linguistics |
URN: | urn:nbn:de:bvb:19-epub-72189-8 |
ISBN: | 978-1-950737-48-2 |
Place of Publication: | Stroudsburg, USA |
Language: | English |
Item ID: | 72189 |
Date Deposited: | 20. May 2020, 09:23 |
Last Modified: | 04. Nov 2020, 13:53 |