HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks

Lidia Pivovarova, Llorenç Escoter, Arto Klami, Roman Yangarber


Abstract
Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news. Our solution for determining the sentiment score extends an earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a data augmentation scheme, and use a larger data collection to complement the small training data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.
Anthology ID:
S17-2143
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:
842–846
Language:
URL:
https://www.aclweb.org/anthology/S17-2143
DOI:
10.18653/v1/S17-2143
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2143.pdf