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

Authors :
Yuanbin Wu
Man Lan
Huimin Xu
Source :
SemEval@NAACL-HLT
Publication Year :
2018
Publisher :
Association for Computational Linguistics, 2018.

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.

Details

Database :
OpenAIRE
Journal :
Proceedings of The 12th International Workshop on Semantic Evaluation
Accession number :
edsair.doi...........9b319af61b93bc9a2f1c0925420c759c
Full Text :
https://doi.org/10.18653/v1/s18-1035