Leveraging News Sentiment to Improve Microblog Sentiment Classification in the Financial Domain

Tobias Daudert, Paul Buitelaar, Sapna Negi


Abstract
With the rising popularity of social media in the society and in research, analysing texts short in length, such as microblogs, becomes an increasingly important task. As a medium of communication, microblogs carry peoples sentiments and express them to the public. Given that sentiments are driven by multiple factors including the news media, the question arises if the sentiment expressed in news and the news article themselves can be leveraged to detect and classify sentiment in microblogs. Prior research has highlighted the impact of sentiments and opinions on the market dynamics, making the financial domain a prime case study for this approach. Therefore, this paper describes ongoing research dealing with the exploitation of news contained sentiment to improve microblog sentiment classification in a financial context.
Anthology ID:
W18-3107
Volume:
Proceedings of the First Workshop on Economics and Natural Language Processing
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
49–54
Language:
URL:
https://aclanthology.org/W18-3107
DOI:
10.18653/v1/W18-3107
Bibkey:
Cite (ACL):
Tobias Daudert, Paul Buitelaar, and Sapna Negi. 2018. Leveraging News Sentiment to Improve Microblog Sentiment Classification in the Financial Domain. In Proceedings of the First Workshop on Economics and Natural Language Processing, pages 49–54, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Leveraging News Sentiment to Improve Microblog Sentiment Classification in the Financial Domain (Daudert et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-3107.pdf