DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles

Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis, Avi Arampatzis


Abstract
We present the system developed by the team DUTH for the participation in Semeval-2017 task 5 - Fine-Grained Sentiment Analysis on Financial Microblogs and News, in subtasks A and B. Our approach to determine the sentiment of Microblog Messages and News Statements & Headlines is based on linguistic preprocessing, feature engineering, and supervised machine learning techniques. To train our model, we used Neural Network Regression, Linear Regression, Boosted Decision Tree Regression and Decision Forrest Regression classifiers to forecast sentiment scores. At the end, we present an error measure, so as to improve the performance about forecasting methods of the system.
Anthology ID:
S17-2147
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:
861–865
Language:
URL:
https://www.aclweb.org/anthology/S17-2147
DOI:
10.18653/v1/S17-2147
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2147.pdf