@inproceedings{li-etal-2018-isclab,
title = "{ISCLAB} at {S}em{E}val-2018 Task 1: {UIR}-Miner for Affect in Tweets",
author = "Li, Meng and
Dong, Zhenyuan and
Fan, Zhihao and
Meng, Kongming and
Cao, Jinghua and
Ding, Guanqi and
Liu, Yuhan and
Shan, Jiawei and
Li, Binyang",
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/S18-1042",
doi = "10.18653/v1/S18-1042",
pages = "286--290",
abstract = "This paper presents a UIR-Miner system for emotion and sentiment analysis evaluation in Twitter in SemEval 2018. Our system consists of three main modules: preprocessing module, stacking module to solve the intensity prediction of emotion and sentiment, LSTM network module to solve multi-label classification, and the hierarchical attention network module for solving emotion and sentiment classification problem. According to the metrics of SemEval 2018, our system gets the final scores of 0.636, 0.531, 0.731, 0.708, and 0.408 on 5 subtasks, respectively.",
}
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<abstract>This paper presents a UIR-Miner system for emotion and sentiment analysis evaluation in Twitter in SemEval 2018. Our system consists of three main modules: preprocessing module, stacking module to solve the intensity prediction of emotion and sentiment, LSTM network module to solve multi-label classification, and the hierarchical attention network module for solving emotion and sentiment classification problem. According to the metrics of SemEval 2018, our system gets the final scores of 0.636, 0.531, 0.731, 0.708, and 0.408 on 5 subtasks, respectively.</abstract>
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%0 Conference Proceedings
%T ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets
%A Li, Meng
%A Dong, Zhenyuan
%A Fan, Zhihao
%A Meng, Kongming
%A Cao, Jinghua
%A Ding, Guanqi
%A Liu, Yuhan
%A Shan, Jiawei
%A Li, Binyang
%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 li-etal-2018-isclab
%X This paper presents a UIR-Miner system for emotion and sentiment analysis evaluation in Twitter in SemEval 2018. Our system consists of three main modules: preprocessing module, stacking module to solve the intensity prediction of emotion and sentiment, LSTM network module to solve multi-label classification, and the hierarchical attention network module for solving emotion and sentiment classification problem. According to the metrics of SemEval 2018, our system gets the final scores of 0.636, 0.531, 0.731, 0.708, and 0.408 on 5 subtasks, respectively.
%R 10.18653/v1/S18-1042
%U https://aclanthology.org/S18-1042
%U https://doi.org/10.18653/v1/S18-1042
%P 286-290
Markdown (Informal)
[ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets](https://aclanthology.org/S18-1042) (Li et al., SemEval 2018)
ACL
- Meng Li, Zhenyuan Dong, Zhihao Fan, Kongming Meng, Jinghua Cao, Guanqi Ding, Yuhan Liu, Jiawei Shan, and Binyang Li. 2018. ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 286–290, New Orleans, Louisiana. Association for Computational Linguistics.