IEICE TRANSACTIONS on Information
Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants
Summary :
Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. Detecting and analyzing user stances from massive opinion-oriented twitter posts provide enormous opportunities to journalists, governments, companies, and other organizations. Most of the prior studies have explored the traditional deep learning models, e.g., long short-term memory (LSTM) and gated recurrent unit (GRU) for detecting stance in tweets. However, compared to these traditional approaches, recently proposed densely connected bidirectional LSTM and nested LSTMs architectures effectively address the vanishing-gradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural network model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higher-level tweet representations. Results of extensive experiments on single and multi-target benchmark stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.12 pp.2493-2503
- Publication Date
- 2019/12/01
- Publicized
- 2019/09/25
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDP7080
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Umme Aymun SIDDIQUA
Toyohashi University of Technology
Abu Nowshed CHY
Toyohashi University of Technology
Masaki AONO
Toyohashi University of Technology
Keyword
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Umme Aymun SIDDIQUA, Abu Nowshed CHY, Masaki AONO, "Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 12, pp. 2493-2503, December 2019, doi: 10.1587/transinf.2019EDP7080.
Abstract: Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. Detecting and analyzing user stances from massive opinion-oriented twitter posts provide enormous opportunities to journalists, governments, companies, and other organizations. Most of the prior studies have explored the traditional deep learning models, e.g., long short-term memory (LSTM) and gated recurrent unit (GRU) for detecting stance in tweets. However, compared to these traditional approaches, recently proposed densely connected bidirectional LSTM and nested LSTMs architectures effectively address the vanishing-gradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural network model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higher-level tweet representations. Results of extensive experiments on single and multi-target benchmark stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7080/_p
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@ARTICLE{e102-d_12_2493,
author={Umme Aymun SIDDIQUA, Abu Nowshed CHY, Masaki AONO, },
journal={IEICE TRANSACTIONS on Information},
title={Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants},
year={2019},
volume={E102-D},
number={12},
pages={2493-2503},
abstract={Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. Detecting and analyzing user stances from massive opinion-oriented twitter posts provide enormous opportunities to journalists, governments, companies, and other organizations. Most of the prior studies have explored the traditional deep learning models, e.g., long short-term memory (LSTM) and gated recurrent unit (GRU) for detecting stance in tweets. However, compared to these traditional approaches, recently proposed densely connected bidirectional LSTM and nested LSTMs architectures effectively address the vanishing-gradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural network model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higher-level tweet representations. Results of extensive experiments on single and multi-target benchmark stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.},
keywords={},
doi={10.1587/transinf.2019EDP7080},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants
T2 - IEICE TRANSACTIONS on Information
SP - 2493
EP - 2503
AU - Umme Aymun SIDDIQUA
AU - Abu Nowshed CHY
AU - Masaki AONO
PY - 2019
DO - 10.1587/transinf.2019EDP7080
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2019
AB - Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. Detecting and analyzing user stances from massive opinion-oriented twitter posts provide enormous opportunities to journalists, governments, companies, and other organizations. Most of the prior studies have explored the traditional deep learning models, e.g., long short-term memory (LSTM) and gated recurrent unit (GRU) for detecting stance in tweets. However, compared to these traditional approaches, recently proposed densely connected bidirectional LSTM and nested LSTMs architectures effectively address the vanishing-gradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural network model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higher-level tweet representations. Results of extensive experiments on single and multi-target benchmark stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.
ER -
English
