@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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2017-ynu-hpcc">
<titleInfo>
<title>YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction</title>
</titleInfo>
<name type="personal">
<namePart type="given">You</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hang</namePart>
<namePart type="family">Yuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jin</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xuejie</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alexandra</namePart>
<namePart type="family">Balahur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saif</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Mohammad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Erik</namePart>
<namePart type="family">van der Goot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Copenhagen, Denmark</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">zhang-etal-2017-ynu-hpcc</identifier>
<identifier type="doi">10.18653/v1/W17-5227</identifier>
<location>
<url>https://aclanthology.org/W17-5227</url>
</location>
<part>
<date>2017-09</date>
<extent unit="page">
<start>200</start>
<end>204</end>
</extent>
</part>
</mods>
</modsCollection>
%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