Exploring Various Linguistic Features for Stance Detection

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

doi:10.1007/978-3-319-50496-4_76

Cited by 9 publications

(9 citation statements)

References 5 publications

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“…Opinions are not always explicitly expressed, and can also be implicit, explicit, ironic, metaphoric, uncertain, etc. Sun et al (2016), Al-Ayyoub et al (2018), and Simaki et al (2018, which make stance detection more challenging. Moreover, surrounding words and symbols can alter the stance of an utterance (Sun et al, 2016), e.g.…”

Section: Stance Utterancementioning

confidence: 99%

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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: Stance Utterancementioning

confidence: 99%

“…Sun et al (2016), Al-Ayyoub et al (2018), and Simaki et al (2018, which make stance detection more challenging. Moreover, surrounding words and symbols can alter the stance of an utterance (Sun et al, 2016), e.g. negating words or ironic emojis inverting the meaning of a text, which are especially challenging to detect.…”

Section: Stance Utterancementioning

confidence: 99%

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

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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“…For example, the tweet ''Jeb bush is the only sane candidate in this republican lineup, I support him'' will be assigned positive by sentiment analysis [140], [143], but extracted with 'against' stance to the topic ''Donald Trump as President'' by stance detection. Research on stance detection can be categorized into four groups based on debate settings, such as congressional floor debates [6]- [9], company-internal discussions [10], [11], online forums ideological debates [12]- [27] and hot-event oriented debates on social media [28]- [50]. The latter two are open domain and flexible, therefore more challengeable.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Opinion Mining: From Stance to Product Aspect

Wang¹,

Zhou²,

Jiang³

et al. 2019

IEEE Access

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With the prevalence of social media and online forum, opinion mining, aiming at analyzing and discovering the latent opinion in user-generated reviews on the Internet, has become a hot research topic. This survey focuses on two important subtasks in this field, stance detection and product aspect mining, both of which can be formalized as the problem of the triple target, aspect, opinion extraction. In this paper, we first introduce the general framework of opinion mining and describe the evaluation metrics. Then, the methodologies for stance detection on different sources, such as online forum and social media are discussed. After that, approaches for product aspect mining are categorized into three main groups which are corpus level aspect extraction, corpus level aspect, and opinion mining, and document level aspect and opinion mining based on the processing units and tasks. And then we discuss the challenges and possible solutions. Finally, we summarize the evolving trend of the reviewed methodologies and conclude the survey. INDEX TERMS Opinion mining, stance detection, product aspect mining, topic model, deep neural network.

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“…To the best of my knowledge, beside my own research, Sun et al [2016] are the only authors that have published some work, in which syntactic features are explored for SD. In their approach, as participants in the NLPCC-ICCPOL 2016 Task 4, Xu et al [2016] exploit both morphological features, connecting each word to its PoS tag and syntactic features, exploiting dependency trees, connecting two words that are in a dependency relation between themselves and also exploiting the "paths" that connect the root of a sentence to each "leaf" of the syntactic tree.…”

Section: Machine Learning Approaches For Stance Detectionmentioning

confidence: 99%

“…As anticipated in Section 3.2, as I was able to verify, beside my own research there are very few works of NLP that have explored the contribution of syntax as feature for SD. The only authors that have published some work on this regard are Sun et al [2016], who, as participants in the NLPCC-ICCPOL 2016 shared task on SD in Chinese, exploit both morphological and syntactic features, connecting words that are in a dependency relation between each other. Their research was conducted solely on data in Chinese, extracted from Sina Weibo and Twitter.…”

Section: Stance Detection Using Dependency Syntaxmentioning

confidence: 99%

Dependency Syntax in the Automatic Detection of Irony and Stance

Cignarella¹

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To all the people who inspired me and supported me during this amazing and life-changing journey.To myself. 1 1 I started writing the first version of this dissertation during the first health emergency lockdown due to the outbreak of the Covid-19 pandemic in Italy (starting from March 2020). I was able to submit the first draft in April 2021, and I later received the comments of reviewers around July 2021. The final (and current) version of the thesis has been registered in September 2021.

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Exploring Various Linguistic Features for Stance Detection

Cited by 9 publications

References 5 publications

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

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

A Survey on Opinion Mining: From Stance to Product Aspect

Dependency Syntax in the Automatic Detection of Irony and Stance

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