Stance and Sentiment in Czech

Hercig, Tomáš; Krejzl, Peter; Král, Pavel

doi:10.13053/cys-22-3-3014

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…In general, existing datasets cover short time spans in languages including English (Ferreira & Vlachos, 2016;Mohammad et al, 2016b;Simaki et al, 2017;Somasundaran & Wiebe, 2009;Hercig et al, 2018;Anand et al, 2011;Conforti et al, 2020), Arabic Addawood et al, 2018), Italian (Lai et al, 2018), Chinese (Xu et al, 2016), Turkish (Küc ¸ük & Can, 2020), Spanish and Catalan (Taulé et al, 2018), Kannada (Skanda et al, 2017), German (Zubiaga et al, 2016), Russian (Lozhnikov et al, 2018).…”

Section: Core Challengesmentioning

confidence: 99%

Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.

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Section: Core Challengesmentioning

confidence: 99%

Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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“…In general, existing datasets are of limited and short time spans in languages including English [28,62,80,84,84,42,95,5,19], Arabic [10,2], Italian [51], Chinese [98], Turkish [47], Spanish and Catalan [88], Kannada [82], German [103], Russian [58]. Recent efforts in multilingual stance classification have also published datasets including German, French and Italian [64,91], and English, French, Italian, Spanish and Catalan [53], but still restricted in terms of the timeframe covered.…”

Section: Core Challengesmentioning

confidence: 99%

Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions

Alkhalifa¹,

Zubiaga²

2021

Preprint

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Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets with limited time coverage, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require in turn adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review in emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance particularly. We further discuss current directions in capturing stance dynamics in social media. We organise the challenges of dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.

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“…Stance and Sentiment Sentiment Analysis has a long history of association with stance (Somasundaran, Ruppenhofer, and Wiebe 2007;Somasundaran and Wiebe 2010). Sentiment is often annotated in parallel to stance (Mohammad et al 2016;Hercig, Krejzl, and Král 2018) and has been used extensively as a feature (Ebrahimi, Dou, and Lowd 2016;Sobhani, Mohammad, and Kiritchenko 2016;Sun et al 2018) or as an auxiliary task (Li and Caragea 2019;Sun et al 2019) for improving stance detection. Missing from these studies, however, is leveraging sentiment annotations to generate noisy stance examples, which we explore here: for English and in a multilingual setting.…”

Section: Cross-lingual Stance Detectionmentioning

confidence: 99%

Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training

Hardalov¹,

Arora²,

Nakov³

et al. 2021

Preprint

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The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languages depending on the user and the platform, which can be a local news outlet, a social media platform, a news forum, etc. Most research in stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection. Moreover, non-English sources of labelled data are often scarce and present additional challenges. Recently, large multilingual language models have substantially improved the performance on many non-English tasks, especially such with limited numbers of examples. This highlights the importance of model pre-training and its ability to learn from few examples. In this paper, we present the most comprehensive study of cross-lingual stance detection to date: we experiment with 15 diverse datasets in 12 languages from 6 language families, and with 6 low-resource evaluation settings each. For our experiments, we build on pattern-exploiting training, proposing the addition of a novel label encoder to simplify the verbalisation procedure. We further propose sentiment-based generation of stance data for pre-training, which shows sizeable improvement of more than 6% F1 absolute in low-shot settings compared to several strong baselines.

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Stance and Sentiment in Czech

Cited by 9 publications

References 9 publications

Capturing stance dynamics in social media: open challenges and research directions

Capturing stance dynamics in social media: open challenges and research directions

Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions

Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training

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