@inproceedings{zhang-etal-2017-ynu-hpcc,
title = "{YNU}-{HPCC} at {E}mo{I}nt-2017: Using a {CNN}-{LSTM} Model for Sentiment Intensity Prediction",
author = "Zhang, You and
Yuan, Hang and
Wang, Jin and
Zhang, Xuejie",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
van der Goot, Erik",
booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5227/",
doi = "10.18653/v1/W17-5227",
pages = "200--204",
abstract = "In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to complete the task. The CNN-LSTM model has two combined parts: CNN extracts local n-gram features within tweets and LSTM composes the features to capture long-distance dependency across tweets. Additionally, we used other three models (CNN, LSTM, BiLSTM) as baseline algorithms. Our introduced model showed good performance in the experimental results."
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%0 Conference Proceedings
%T YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction
%A Zhang, You
%A Yuan, Hang
%A Wang, Jin
%A Zhang, Xuejie
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y van der Goot, Erik
%S Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F zhang-etal-2017-ynu-hpcc
%X In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to complete the task. The CNN-LSTM model has two combined parts: CNN extracts local n-gram features within tweets and LSTM composes the features to capture long-distance dependency across tweets. Additionally, we used other three models (CNN, LSTM, BiLSTM) as baseline algorithms. Our introduced model showed good performance in the experimental results.
%R 10.18653/v1/W17-5227
%U https://aclanthology.org/W17-5227/
%U https://doi.org/10.18653/v1/W17-5227
%P 200-204
Markdown (Informal)
[YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction](https://aclanthology.org/W17-5227/) (Zhang et al., WASSA 2017)
ACL