YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction

You Zhang, Hang Yuan, Jin Wang, Xuejie Zhang


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.
Anthology ID:
W17-5227
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Alexandra Balahur, Saif M. Mohammad, Erik van der Goot
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
200–204
Language:
URL:
https://aclanthology.org/W17-5227
DOI:
10.18653/v1/W17-5227
Bibkey:
Cite (ACL):
You Zhang, Hang Yuan, Jin Wang, and Xuejie Zhang. 2017. YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 200–204, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction (Zhang et al., WASSA 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5227.pdf