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ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models
- 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.
- Subjects :
- Computer science
business.industry
02 engineering and technology
Lexicon
Machine learning
computer.software_genre
SemEval
Domain (software engineering)
Task (project management)
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
Emotion intensity
business
computer
Subjects
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