@inproceedings{su-etal-2021-soochowds,
title = "{S}oochow{DS} at {ROCLING}-2021 Shared Task: Text Sentiment Analysis Using {BERT} and {LSTM}",
author = "Su, Ruei-Cyuan and
Chong, Sig-Seong and
Su, Tzu-En and
Su, Ming-Hsiang",
editor = "Lee, Lung-Hao and
Chang, Chia-Hui and
Chen, Kuan-Yu",
booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
month = oct,
year = "2021",
address = "Taoyuan, Taiwan",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
url = "https://aclanthology.org/2021.rocling-1.49/",
pages = "375--379",
abstract = "In this shared task, this paper proposes a method to combine the BERT-based word vector model and the LSTM prediction model to predict the Valence and Arousal values in the text. Among them, the BERT-based word vector is 768-dimensional, and each word vector in the sentence is sequentially fed to the LSTM model for prediction. The experimental results show that the performance of our proposed method is better than the results of the Lasso Regression model."
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%0 Conference Proceedings
%T SoochowDS at ROCLING-2021 Shared Task: Text Sentiment Analysis Using BERT and LSTM
%A Su, Ruei-Cyuan
%A Chong, Sig-Seong
%A Su, Tzu-En
%A Su, Ming-Hsiang
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F su-etal-2021-soochowds
%X In this shared task, this paper proposes a method to combine the BERT-based word vector model and the LSTM prediction model to predict the Valence and Arousal values in the text. Among them, the BERT-based word vector is 768-dimensional, and each word vector in the sentence is sequentially fed to the LSTM model for prediction. The experimental results show that the performance of our proposed method is better than the results of the Lasso Regression model.
%U https://aclanthology.org/2021.rocling-1.49/
%P 375-379
Markdown (Informal)
[SoochowDS at ROCLING-2021 Shared Task: Text Sentiment Analysis Using BERT and LSTM](https://aclanthology.org/2021.rocling-1.49/) (Su et al., ROCLING 2021)
ACL