Neural Stance Detection With Hierarchical Linguistic Representations

Wang, Zhongqing; Sun, Qingying; Li, Shoushan; Zhu, Qiaoming; Zhou, Guodong

doi:10.1109/taslp.2020.2963954

Cited by 13 publications

(10 citation statements)

References 30 publications

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“…In order not to miss the denier attitude of a single tweet that could interfere with the implementation of climate change policies, we focus on detecting statementlevel stance detection, where the goal is to predict the stance described in a single tweet. The stance detection of tweets has been studied in a variety of work on the popular SemEVAL 2016 dataset, which includes the 5 targets including climate change (with 364 climate change tweets) (Vychegzhanin and Kotelnikov 2021;Wang et al 2020). However, in these previous studies, little attention was paid to understanding the characteristics of climate change denier and believer tweets in particular (only 29 climate change denier tweets in the SemEVAL 2016 dataset).…”

Section: Stance Detectionmentioning

confidence: 99%

“…Some of the works on stance recognition have emphasized the importance of sentiment (Wang et al 2020), while some have cited the orthogonal relationship between stance and sentiment of the statement (Sen, Flöck, and Wagner 2020). However, several works have focused on the sentimental aspects of climate change conversations and justified their role in climate change (Cody et al 2015;Jiang et al 2017).…”

Section: Sentiment Analysismentioning

confidence: 99%

“…They evaluate the performance of the model using the UKP Sentential Argument Mining Corpus and the SemEval-2016 dataset. HAN (Wang et al 2020)…”

Section: Baseline Techniquesmentioning

confidence: 99%

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A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets

Upadhyaya¹,

Fisichella²,

Nejdl³

2022

Preprint

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Climate change has become one of the biggest challenges of our time. Social media platforms such as Twitter play an important role in raising public awareness and spreading knowledge about the dangers of the current climate crisis. With the increasing number of campaigns and communication about climate change through social media, the information could create more awareness and reach the general public and policy makers. However, these Twitter communications lead to polarization of beliefs, opinion-dominated ideologies, and often a split into two communities of climate change deniers and believers. In this paper, we propose a framework that helps identify denier statements on Twitter and thus classifies the stance of the tweet into one of the two attitudes towards climate change (denier/believer). The sentimental aspects of Twitter data on climate change are deeply rooted in general public attitudes toward climate change. Therefore, our work focuses on learning two closely related tasks: Stance Detection and Sentiment Analysis of climate change tweets. We propose a multi-task framework that performs stance detection (primary task) and sentiment analysis (auxiliary task) simultaneously. The proposed model incorporates the feature-specific and shared-specific attention frameworks to fuse multiple features and learn the generalized features for both tasks. The experimental results show that the proposed framework increases the performance of the primary task, i.e., stance detection by benefiting from the auxiliary task, i.e., sentiment analysis compared to its uni-modal and single-task variants.

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Section: Stance Detectionmentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

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A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets

Upadhyaya¹,

Fisichella²,

Nejdl³

2022

Preprint

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“…This type of neural network model is found to be better than LSTM-RNN while considering long-term dependency. Zhongqing Wang et al [41] proposed the Hierarchical Attention Model in 2020 using Linguistic Attention based on argument representation, dependency representation and sentiment representation to extract meaningful words.…”

Section: B Limitationsmentioning

confidence: 99%

A Meta Analysis of Attention Models on Legal Judgment Prediction System

Sukanya¹,

J.Priyadarshini²

2021

IJACSA

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Intelligence in legal research is transforming the legal area in manifold ways. Pendency of court cases is a long-lasting problem in the judiciary due to various reasons such as lack of judges, lack of technology in legal services and the legal loopholes. The judicial system has to be more competent and more reliable in providing justice on time. One of the major causes of pending cases is the lack of legal intelligence to assist the litigants. The study in this paper reviews the challenges faced by judgment prediction system due to lengthy case facts using deep learning model. The Legal Judgment prediction system can help lawyers, judges and civilians to predict the win or loss rate, punishment term and applicable law articles for new cases. Besides, the paper reviews current encoding and decoding architecture with attention mechanism of transformer model that can be used for Legal Judgment Prediction system. Natural Language Processing using deep learning is an exploring field and there is a need for research to evaluate the current state of the art at the intersection of good text processing and feature representation with a deep learning model. This paper aims to develop a systematic review of existing methods used in the legal judgment prediction system and about the Hierarchical Attention Neural network model in detail. This can also be used in other applications such as legal document classification, sentimental analysis, news classification, text translation, medical reports and so on.

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“…There is a multi-task Attention-based model proposed in [15], which trains Attention besides feeding important sentiment words and applying this model for train and use on Stance-based Target. Another architecture proposed in [16] is a hierarchical model that contains Hierarchical Attention layers and one Attention layer above all of them. This model uses and weights outputs of NLP modules such as sentiment analysis, dependency parsing, and agreement in the model.…”

Section: Related Workmentioning

confidence: 99%

ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language

Jaziriyan

Akbari

Karbasi

2021

2021 11th International Conference on Computer Engineering and Knowledge (ICCKE)

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Target-based Stance Detection is the task of finding a stance toward a target. Twitter is one of the primary sources of political discussions in social media and one of the best resources to analyze Stance toward entities. This work proposes a new method toward Target-based Stance detection by using the stance of replies toward a most important and arguing target in source tweet. This target is detected with respect to the source tweet itself and not limited to a set of pre-defined targets which is the usual approach of the current state-of-the-art methods. Our proposed new attitude resulted in a new corpus called ExaASC for the Arabic Language, one of the low resource languages in this field. In the end, we used BERT to evaluate our corpus and reached a 70.69 Macro F-score. This shows that our data and model can work in a general Target-base Stance Detection system. The corpus is publicly available 1 .

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Neural Stance Detection With Hierarchical Linguistic Representations

Cited by 13 publications

References 30 publications

A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets

A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets

A Meta Analysis of Attention Models on Legal Judgment Prediction System

ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language

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