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
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 861–865
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/S17-2147/
- DOI:
- 10.18653/v1/S17-2147
- Cite (ACL):
- Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis, and Avi Arampatzis. 2017. DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 861–865, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles (Symeonidis et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/S17-2147.pdf