Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants

Umme Aymun SIDDIQUA  Abu Nowshed CHY  Masaki AONO  

IEICE TRANSACTIONS on Information and Systems   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
stance detection,  deep learning,  neural network model,  attention mechanism,  nested LSTMs (NLSTMs),  densely connected bidirectional LSTM (Bi-LSTM),  multi-kernel convolution,  

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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.