ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models

Huimin Xu; Man Lan; Yuanbin Wu

doi:10.18653/v1/s18-1035

ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models

Huimin Xu
, Man Lan^*
, Yuanbin Wu

^*Corresponding author for this work

School of Computer Science and Technology

East China Normal University
Shanghai Key Laboratory of Multidimensional Information Processing

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

6 Scopus citations

Abstract

In this paper we describe our systems submitted to Semeval 2018 Task 1 “Affect in Tweet” (Mohammad et al., 2018). We participated in all subtasks of English tweets, including emotion intensity classification and quantification, valence intensity classification and quantification. In our systems, we extracted four types of features, including linguistic, sentiment lexicon, emotion lexicon and domain-specific features, then fed them to different regressors, finally combined the models to create an ensemble for the better performance. Officially released results showed that our system can be further extended.

Original language	English
Title of host publication	NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop
Editors	Marianna Apidianaki, Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Publisher	Association for Computational Linguistics (ACL)
Pages	231-235
Number of pages	5
ISBN (Electronic)	9781948087209
DOIs	https://doi.org/10.18653/v1/s18-1035
State	Published - 2018
Event	12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States Duration: 5 Jun 2018 → 6 Jun 2018

Publication series

Name	NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop

Conference

Conference	12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
Country/Territory	United States
City	New Orleans
Period	5/06/18 → 6/06/18

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10.18653/v1/s18-1035

Cite this

Xu, H., Lan, M., & Wu, Y. (2018). ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models. In M. Apidianaki, M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, & M. Carpuat (Eds.), NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop (pp. 231-235). (NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s18-1035

Xu, Huimin ; Lan, Man ; Wu, Yuanbin. / ECNU at SemEval-2018 Task 1 : Emotion Intensity Prediction Using Effective Features and Machine Learning Models. NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. editor / Marianna Apidianaki ; Marianna Apidianaki ; Saif M. Mohammad ; Jonathan May ; Ekaterina Shutova ; Steven Bethard ; Marine Carpuat. Association for Computational Linguistics (ACL), 2018. pp. 231-235 (NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop).

@inproceedings{8c09cb9b54014e33b9203367b075d99d,

title = "ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models",

abstract = "In this paper we describe our systems submitted to Semeval 2018 Task 1 “Affect in Tweet” (Mohammad et al., 2018). We participated in all subtasks of English tweets, including emotion intensity classification and quantification, valence intensity classification and quantification. In our systems, we extracted four types of features, including linguistic, sentiment lexicon, emotion lexicon and domain-specific features, then fed them to different regressors, finally combined the models to create an ensemble for the better performance. Officially released results showed that our system can be further extended.",

author = "Huimin Xu and Man Lan and Yuanbin Wu",

note = "Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics; 12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 ; Conference date: 05-06-2018 Through 06-06-2018",

year = "2018",

doi = "10.18653/v1/s18-1035",

language = "英语",

series = "NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop",

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

pages = "231--235",

editor = "Marianna Apidianaki and Marianna Apidianaki and Mohammad, \{Saif M.\} and Jonathan May and Ekaterina Shutova and Steven Bethard and Marine Carpuat",

booktitle = "NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop",

address = "澳大利亚",

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Xu, H, Lan, M & Wu, Y 2018, ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models. in M Apidianaki, M Apidianaki, SM Mohammad, J May, E Shutova, S Bethard & M Carpuat (eds), NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop, Association for Computational Linguistics (ACL), pp. 231-235, 12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018, New Orleans, United States, 5/06/18. https://doi.org/10.18653/v1/s18-1035

ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models. / Xu, Huimin; Lan, Man; Wu, Yuanbin.
NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. ed. / Marianna Apidianaki; Marianna Apidianaki; Saif M. Mohammad; Jonathan May; Ekaterina Shutova; Steven Bethard; Marine Carpuat. Association for Computational Linguistics (ACL), 2018. p. 231-235 (NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop).

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

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AB - In this paper we describe our systems submitted to Semeval 2018 Task 1 “Affect in Tweet” (Mohammad et al., 2018). We participated in all subtasks of English tweets, including emotion intensity classification and quantification, valence intensity classification and quantification. In our systems, we extracted four types of features, including linguistic, sentiment lexicon, emotion lexicon and domain-specific features, then fed them to different regressors, finally combined the models to create an ensemble for the better performance. Officially released results showed that our system can be further extended.

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Xu H, Lan M, Wu Y. ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models. In Apidianaki M, Apidianaki M, Mohammad SM, May J, Shutova E, Bethard S, Carpuat M, editors, NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. Association for Computational Linguistics (ACL). 2018. p. 231-235. (NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop). doi: 10.18653/v1/s18-1035

ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models

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