Modeling Transferable Topics for Cross-Target Stance Detection

Wei, Penghui; Mao, Wenji

doi:10.1145/3331184.3331367

Cited by 37 publications

(44 citation statements)

References 9 publications

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“…The second type of approach attempts to address this transfer learning problem with concept-level knowledge shared by two targets. For example, Wei and Mao (2019) proposed a variational Transfer Network (VTN) method, which complements the commonly used knowledge by inferring the latent topics shared by the two targets.…”

Section: Cross-target Stance Detectionmentioning

confidence: 99%

“…Each tweet is classified as "favor", "against" or "none". Following the previous work (Wei and Mao, 2019), we use the tweets from four targets, including Donald Trump (DT), Hillary Clinton (HC), Legalization of Abortion (LA), and Feminist Movement (FM). These targets are commonly utilized to evaluate the cross-target stance classification.…”

Section: Experimental Datamentioning

confidence: 99%

“…Two evaluation metrics are adopted to verify our SEKT model. First, following (Wei and Mao, 2019), we leverage the average F1-score as one evaluation metric (denoted as F avg ). Second, since the targets in the dataset are imbalanced, we also compute both the micro-averaged F1 (dominating large class) and macro-averaged F1 (dominating small class), and treat their average as another evaluation metric:…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Conventional stance detection methods are struggling to cope well with the data across targets. This motivates the studies of cross-target stance detection (Wei and Mao, 2019), which infers the attitude of the destination target by leveraging a large amount of annotated data from the source target.…”

Section: Introductionmentioning

confidence: 99%

“…So far, several previous studies have been conducted for cross-target stance detection (Augenstein et al, 2016;Wei and Mao, 2019). These methods leverage either common words or concept-level knowledge shared by different targets to bridge the knowledge gap across the different targets.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge

Zhang¹,

Yang²,

Li³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Stance detection is an important task, which aims to classify the attitude of an opinionated text towards a given target. Remarkable success has been achieved when sufficient labeled training data is available. However, annotating sufficient data is labor-intensive, which establishes significant barriers for generalizing the stance classifier to the data with new targets. In this paper, we proposed a Semantic-Emotion Knowledge Transferring (SEKT) model for cross-target stance detection, which uses the external knowledge (semantic and emotion lexicons) as a bridge to enable knowledge transfer across different targets. Specifically, a semantic-emotion heterogeneous graph is constructed from external semantic and emotion lexicons, which is then fed into a graph convolutional network to learn multi-hop semantic connections between words and emotion tags. Then, the learned semantic-emotion graph representation, which serves as prior knowledge bridging the gap between the source and target domains, is fully integrated into the bidirectional long short-term memory (BiLSTM) stance classifier by adding a novel knowledgeaware memory unit to the BiLSTM cell. Extensive experiments on a large real-world dataset demonstrate the superiority of SEKT against the state-of-the-art baseline methods.

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Section: Cross-target Stance Detectionmentioning

confidence: 99%

Section: Experimental Datamentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge

Zhang¹,

Yang²,

Li³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

View full text Add to dashboard Cite

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Stance detection for online public opinion awareness: An overview

Cao

Luo

et al. 2022

Int J of Intelligent Sys

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Stance detection, which focuses on users' deep attitudes, is an important way to understand the online public opinion. This paper presents an overview of stance detection. First, we present a general framework for stance detection, and the main steps of the framework are introduced in detail. The state‐of‐the‐art stance detection methods are categorized into three classes: feature‐based methods, deep learning‐based methods, and ensemble learning‐based methods. Moreover, the advantages and limitations of the existing methods are analyzed. The survey findings show that hybrid‐neural network‐based methods are superior to the other methods. In addition, existing methods still need to pay more attention to the sentiment information, user‐interaction, and attempt to merge more external knowledge to help improve the effect of stance detection.

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