@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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<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.</abstract>
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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
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)
ACL