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

^*此作品的通讯作者

计算机科学与技术学院

East China Normal University
Shanghai Key Laboratory of Multidimensional Information Processing

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

5 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop
出版商	Association for Computational Linguistics (ACL)
页	812-816
页数	5
ISBN（电子版）	9781945626555
DOI	https://doi.org/10.18653/v1/S17-2137
出版状态	已出版 - 2017
活动	11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, 加拿大期限: 3 8月 2017 → 4 8月 2017

丛书

姓名	Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN（印刷版）	0736-587X

会议

会议	11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
国家/地区	加拿大
市	Vancouver
时期	3/08/17 → 4/08/17

访问文件

10.18653/v1/S17-2137

其它文件与链接

链接到 Scopus 的出版物

引用此

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. 在 ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop (页码 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. 页码 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. 在 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), 页码 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, 加拿大, 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. 页码 812-816 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - ECNU at SemEval-2017 Task 4

T2 - 11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

AU - Zhou, Yunxiao

AU - Lan, Man

AU - Wu, Yuanbin

PY - 2017

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.

UR - https://www.scopus.com/pages/publications/85122575186

U2 - 10.18653/v1/S17-2137

DO - 10.18653/v1/S17-2137

M3 - 会议稿件

AN - SCOPUS:85122575186

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

SP - 812

EP - 816

BT - ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop

PB - Association for Computational Linguistics (ACL)

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. 在 ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop. Association for Computational Linguistics (ACL). 2017. 页码 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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