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

Huimin Xu, Man Lan, Yuanbin Wu


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
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
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