NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen


Abstract
Short length, multi-targets, target relation-ship, monetary expressions, and outside reference are characteristics of financial tweets. This paper proposes methods to extract target spans from a tweet and its referencing web page. Total 15 publicly available sentiment dictionaries and one sentiment dictionary constructed from training set, containing sentiment scores in binary or real numbers, are used to compute the sentiment scores of text spans. Moreover, the correlation coeffi-cients of the price return between any two stocks are learned with the price data from Bloomberg. They are used to capture the relationships between the interesting tar-get and other stocks mentioned in a tweet. The best result of our method in both sub-task are 56.68% and 55.43%, evaluated by evaluation method 2.
Anthology ID:
S17-2144
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
847–851
Language:
URL:
https://www.aclweb.org/anthology/S17-2144
DOI:
10.18653/v1/S17-2144
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2144.pdf