@inproceedings{symeonidis-etal-2017-duth-semeval,
title = "{DUTH} at {S}em{E}val-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles",
author = "Symeonidis, Symeon and
Kordonis, John and
Effrosynidis, Dimitrios and
Arampatzis, Avi",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2147",
doi = "10.18653/v1/S17-2147",
pages = "861--865",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles
%A Symeonidis, Symeon
%A Kordonis, John
%A Effrosynidis, Dimitrios
%A Arampatzis, Avi
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F symeonidis-etal-2017-duth-semeval
%X 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.
%R 10.18653/v1/S17-2147
%U https://aclanthology.org/S17-2147
%U https://doi.org/10.18653/v1/S17-2147
%P 861-865
Markdown (Informal)
[DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles](https://aclanthology.org/S17-2147) (Symeonidis et al., SemEval 2017)
ACL