Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets

Zewen Chi, Heyan Huang, Jiangui Chen, Hao Wu, Ran Wei


Abstract
This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets. The term affect refers to emotion-related categories such as anger, fear, etc. Intensity of emo-tions need to be quantified into a real valued score in [0, 1]. We propose an en-semble system including four different deep learning methods which are CNN, Bidirectional LSTM (BLSTM), LSTM-CNN and a CNN-based Attention model (CA). Our system gets an average Pearson correlation score of 0.682 in the subtask EI-reg and an average Pearson correlation score of 0.784 in subtask V-reg, which ranks 17th among 48 systems in EI-reg and 19th among 38 systems in V-reg.
Anthology ID:
S18-1046
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:
313–318
Language:
URL:
https://www.aclweb.org/anthology/S18-1046
DOI:
10.18653/v1/S18-1046
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1046.pdf