@inproceedings{zhou-etal-2017-ecnu,
title = "{ECNU} at {S}em{E}val-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for {T}witter Message Polarity Classification",
author = "Zhou, Yunxiao and
Lan, Man and
Wu, Yuanbin",
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/S17-2137",
doi = "10.18653/v1/S17-2137",
pages = "812--816",
abstract = "This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average.",
}
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%0 Conference Proceedings
%T ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification
%A Zhou, Yunxiao
%A Lan, Man
%A Wu, Yuanbin
%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 zhou-etal-2017-ecnu
%X This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average.
%R 10.18653/v1/S17-2137
%U https://aclanthology.org/S17-2137
%U https://doi.org/10.18653/v1/S17-2137
%P 812-816
Markdown (Informal)
[ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification](https://aclanthology.org/S17-2137) (Zhou et al., SemEval 2017)
ACL