COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines

Kim Schouten, Flavius Frasincar, Franciska de Jong


Abstract
This paper describes our submission to Task 5 of SemEval 2017, Fine-Grained Sentiment Analysis on Financial Microblogs and News, where we limit ourselves to performing sentiment analysis on news headlines only (track 2). The approach presented in this paper uses a Support Vector Machine to do the required regression, and besides unigrams and a sentiment tool, we use various ontology-based features. To this end we created a domain ontology that models various concepts from the financial domain. This allows us to model the sentiment of actions depending on which entity they are affecting (e.g., ‘decreasing debt’ is positive, but ‘decreasing profit’ is negative). The presented approach yielded a cosine distance of 0.6810 on the official test data, resulting in the 12th position.
Anthology ID:
S17-2151
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:
883–887
Language:
URL:
https://www.aclweb.org/anthology/S17-2151
DOI:
10.18653/v1/S17-2151
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2151.pdf