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

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Abstract

This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional NLP, sentiment lexicon, emotion lexicon and domain specific features from tweets, adopted supervised machine learning algorithms to perform emotion intensity prediction.

Anthology ID:: S18-1035
Volume:: Proceedings of the 12th International Workshop on Semantic Evaluation
Month:: June
Year:: 2018
Address:: New Orleans, Louisiana
Editors:: Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:: SemEval
SIG:: SIGLEX
Publisher:: Association for Computational Linguistics
Note:
Pages:: 231–235
Language:
URL:: https://aclanthology.org/S18-1035/
DOI:: 10.18653/v1/S18-1035
Bibkey:
Cite (ACL):: Huimin Xu, Man Lan, and Yuanbin Wu. 2018. ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 231–235, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):: ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models (Xu et al., SemEval 2018)
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PDF:: https://aclanthology.org/S18-1035.pdf

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@inproceedings{xu-etal-2018-ecnu,
    title = "{ECNU} at {S}em{E}val-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models",
    author = "Xu, Huimin  and
      Lan, Man  and
      Wu, Yuanbin",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S18-1035/",
    doi = "10.18653/v1/S18-1035",
    pages = "231--235",
    abstract = "This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional NLP, sentiment lexicon, emotion lexicon and domain specific features from tweets, adopted supervised machine learning algorithms to perform emotion intensity prediction."
}

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%0 Conference Proceedings
%T ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models
%A Xu, Huimin
%A Lan, Man
%A Wu, Yuanbin
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F xu-etal-2018-ecnu
%X This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional NLP, sentiment lexicon, emotion lexicon and domain specific features from tweets, adopted supervised machine learning algorithms to perform emotion intensity prediction.
%R 10.18653/v1/S18-1035
%U https://aclanthology.org/S18-1035/
%U https://doi.org/10.18653/v1/S18-1035
%P 231-235

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Markdown (Informal)

[ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models](https://aclanthology.org/S18-1035/) (Xu et al., SemEval 2018)

ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models (Xu et al., SemEval 2018)

ACL

Huimin Xu, Man Lan, and Yuanbin Wu. 2018. ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 231–235, New Orleans, Louisiana. Association for Computational Linguistics.