ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification

Yunxiao Zhou; Man Lan; Yuanbin Wu

doi:10.18653/v1/S17-2137

ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification

Yunxiao Zhou
, Man Lan^*
, Yuanbin Wu

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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.

Original language	English
Title of host publication	ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop
Publisher	Association for Computational Linguistics (ACL)
Pages	812-816
Number of pages	5
ISBN (Electronic)	9781945626555
DOIs	https://doi.org/10.18653/v1/S17-2137
State	Published - 2017
Event	11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada Duration: 3 Aug 2017 → 4 Aug 2017

Publication series

Name	Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)	0736-587X

Conference

Conference	11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
Country/Territory	Canada
City	Vancouver
Period	3/08/17 → 4/08/17

Access to Document

10.18653/v1/S17-2137

Cite this

Zhou, Y., Lan, M., & Wu, Y. (2017). ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification. In ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop (pp. 812-816). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/S17-2137

Zhou, Yunxiao ; Lan, Man ; Wu, Yuanbin. / ECNU at SemEval-2017 Task 4 : Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification. ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop. Association for Computational Linguistics (ACL), 2017. pp. 812-816 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

@inproceedings{d01d78e9d24848959e07bb71b90b0346,

title = "ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification",

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.",

author = "Yunxiao Zhou and Man Lan and Yuanbin Wu",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics; 11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 ; Conference date: 03-08-2017 Through 04-08-2017",

year = "2017",

doi = "10.18653/v1/S17-2137",

language = "英语",

series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics (ACL)",

pages = "812--816",

booktitle = "ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop",

address = "澳大利亚",

}

Zhou, Y, Lan, M & Wu, Y 2017, ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification. in ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), pp. 812-816, 11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 3/08/17. https://doi.org/10.18653/v1/S17-2137

ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification. / Zhou, Yunxiao; Lan, Man; Wu, Yuanbin.
ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop. Association for Computational Linguistics (ACL), 2017. p. 812-816 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wu, Yuanbin

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Y1 - 2017

N2 - 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.

AB - 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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DO - 10.18653/v1/S17-2137

M3 - 会议稿件

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T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics

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Y2 - 3 August 2017 through 4 August 2017

ER -

Zhou Y, Lan M, Wu Y. ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification. In ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop. Association for Computational Linguistics (ACL). 2017. p. 812-816. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/S17-2137

ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification

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