@inproceedings{chi-etal-2018-zewen,
title = "Zewen at {S}em{E}val-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets",
author = "Chi, Zewen and
Huang, Heyan and
Chen, Jiangui and
Wu, Hao and
Wei, Ran",
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/S18-1046",
doi = "10.18653/v1/S18-1046",
pages = "313--318",
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.",
}
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%0 Conference Proceedings
%T Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets
%A Chi, Zewen
%A Huang, Heyan
%A Chen, Jiangui
%A Wu, Hao
%A Wei, Ran
%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 chi-etal-2018-zewen
%X 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.
%R 10.18653/v1/S18-1046
%U https://aclanthology.org/S18-1046
%U https://doi.org/10.18653/v1/S18-1046
%P 313-318
Markdown (Informal)
[Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets](https://aclanthology.org/S18-1046) (Chi et al., SemEval 2018)
ACL