Entity-Centric Contextual Affective Analysis

Field, Anjalie; Tsvetkov, Yulia

doi:10.18653/v1/p19-1243

Cited by 18 publications

(17 citation statements)

References 24 publications

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“…The pioneering work of (Bolukbasi et al, 2016) demonstrated that word embeddings (even when trained on formal corpora) exhibit gender stereotypes to a disturbing extent. On top of that, several studies have been proposed to measure and mitigate bias in word embeddings (Chaloner and Maldonado, 2019;Zhou et al, 2019; and more recently on pre-trained contextualized embeddings models (Kurita et al, 2019;May et al, 2019;Field and Tsvetkov, 2019;Sheng et al, 2019;Nangia et al, 2020;Vig et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

HONEST: Measuring Hurtful Sentence Completion in Language Models

Nozza¹,

Bianchi²,

Hovy³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that 4.3% of the time, language models complete a sentence with a hurtful word. These cases are not random, but follow language and genderspecific patterns. We propose a score to measure hurtful sentence completions in language models (HONEST). It uses a systematic template-and lexicon-based bias evaluation methodology for six languages. Our findings suggest that these models replicate and amplify deep-seated societal stereotypes about gender roles. Sentence completions refer to sexual promiscuity when the target is female in 9% of the time, and in 4% to homosexuality when the target is male. The results raise questions about the use of these models in production settings.

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Section: Related Workmentioning

confidence: 99%

HONEST: Measuring Hurtful Sentence Completion in Language Models

Nozza¹,

Bianchi²,

Hovy³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…We also study the correlation between entity mentions and moral foundation usage by different groups, which helps pave the way to analyze partisan sentiment towards entities using MFT. In that sense, our work is broadly related to entity-centric affective analysis (Deng and Wiebe, 2015;Field and Tsvetkov, 2019;Park et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory

Roy¹,

Goldwasser²

2021

Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

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The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue. It is widely used to identify sentence-level sentiment. In this paper, we study the nuanced stances and partisan sentiment towards entities of US politicians using Moral Foundation Theory, on two politically divisive issues -Gun Control and Immigration. We define the nuanced stances of the US politicians on these two topics by the grades given by related organizations to the politicians. To conduct this study, we first filter out 74k and 87k tweets on the topics Gun Control and Immigration, respectively, from an existing tweet corpus authored by US parliament members. Then, we identify moral foundations in these tweets using deep relational learning. Finally, doing qualitative and quantitative evaluations on this dataset, we found out that there is a strong correlation between moral foundation usage and politicians' nuanced stances on a particular topic. We also found notable differences in moral foundation usage by different political parties when they address different entities.

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“…To the best of our knowledge, this is the first work that presents a multi-pronged investigation of brands and subjective knowledge like affect attributes represented in contextual representation. Field and Tsvetkov (2019) is the most relevant prior work in terms of affect analysis. They present an entity-centric affective analysis with the use of contextual representations, where they find that meaningful affect information is captured in contextualize word representations but these representations are heavily biased towards their training data.…”

Section: Related Workmentioning

confidence: 99%

What BERTs and GPTs know about your brand? Probing contextual language models for affect associations

Srivastava

Pilli²,

Bhat³

et al. 2021

Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Ar

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Investigating brand perception is fundamental to marketing strategies. In this regard, brand image, defined by a set of attributes (Aaker, 1997), is recognized as a key element in indicating how a brand is perceived by various stakeholders such as consumers and competitors. Traditional approaches (e.g., surveys) to monitor brand perceptions are time-consuming and inefficient. In the era of digital marketing, both brand managers and consumers engage with a vast amount of digital marketing content. The exponential growth of digital content has propelled the emergence of pre-trained language models such as BERT and GPT as essential tools in solving myriads of challenges with textual data. This paper seeks to investigate the extent of brand perceptions (i.e., brand and image attribute associations) these language models encode. We believe that any kind of bias for a brand and attribute pair may influence customer-centric downstream tasks such as recommender systems, sentiment analysis, and question-answering, e.g., suggesting a specific brand consistently when queried for 'innovative' products. We use synthetic data and real-life data and report comparison results for five contextual LMs, viz. BERT, RoBERTa, DistilBERT, ALBERT and BART.

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Entity-Centric Contextual Affective Analysis

Cited by 18 publications

References 24 publications

HONEST: Measuring Hurtful Sentence Completion in Language Models

HONEST: Measuring Hurtful Sentence Completion in Language Models

Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory

What BERTs and GPTs know about your brand? Probing contextual language models for affect associations

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