Moral Stance Recognition and Polarity Classification from Twitter and Elicited Text

Santos, Wesley Ramos dos; Paraboni, Ivandré

doi:10.26615/978-954-452-056-4_123

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…Stance prediction is the computational task of inferring an attitude in favour or against a set target topic (Mohammad et al, 2016;dos Santos and Paraboni, 2019). For instance, 'A universal basic income would alleviate poverty' conveys a stance in favour of the target 'universal basic income'.…”

Section: Downstream Evaluation Tasksmentioning

confidence: 99%

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BERTabaporu: Assessing a Genre-specific Language Model for Portuguese NLP

da Costa,

Pavan,

dos Santos

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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Transformer-based language models such as Bidirectional Encoder Representations from Transformers (BERT) are now mainstream in the NLP field, but extensions to languages other than English, to new domains and/or to more specific text genres are still in demand. In this paper we introduced BERTabaporu, a BERT language model that has been pre-trained on Twitter data in the Brazilian Portuguese language. The model is shown to outperform the best-known general-purpose model for this language in three Twitter-related NLP tasks, making a potentially useful resource for Portuguese NLP in general.

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Section: Downstream Evaluation Tasksmentioning

confidence: 99%

“…For the mental health prediction task, we used two datasets comprising Twitter timelines of individuals with a diagnosis for depression and anxiety disorder described in (dos Santos et al, 2020a(dos Santos et al, , 2023. The depression dataset contains 13.5K timelines, and the anxiety dataset contains 17.8K timelines in total.…”

Section: Task Datasetsmentioning

confidence: 99%

BERTabaporu: Assessing a Genre-specific Language Model for Portuguese NLP

da Costa,

Pavan,

dos Santos

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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“…2018), short essay texts about topics of a moral nature (e.g., abortion legalisation, death penalty, etc.) from the BRmoral corpus (dos Santos and Paraboni 2019; Pavan et al . 2020) and Twitter data from the TwiSty corpus (Verhoeven, Daelemans, and Plank 2016).…”

Section: Authorship Attribution Using Author Profiling Classifiersmentioning

confidence: 99%

Authorship attribution using author profiling classifiers

Deutsch

Paraboni

2022

Nat. Lang. Eng.

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Authorship attribution – the computational task of identifying the author of a given text document within a set of possible candidates – has been attracting interest in Natural Language Processing research for many years. At the same time, significant advances have also been observed in the related field of author profiling, that is, the computational task of learning author demographics from text such as gender, age and others. The close relation between the two topics – both of which focused on gaining knowledge about the individual who wrote a piece of text – suggests that research in these fields may benefit from each other. To illustrate this, this work addresses the issue of author identification with the aid of author profiling methods, adding demographics predictions to an authorship attribution architecture that may be particularly suitable to extensions of this kind, namely, a stack of classifiers devoted to different aspects of the input text (words, characters and text distortion patterns.) The enriched model is evaluated across a range of text domains, languages and author profiling estimators, and its results are shown to compare favourably to those obtained by a standard authorship attribution method that does not have access to author demographics predictions.

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“…Many of the works have focused on data from Twitter, incorporating conversational and interactional context [9,10] in order to better classify the stances of the users in a thread of tweets or simply by taking the tweets in an independent way [11][12][13][14]. The SemEval-2017 task 8 [15] proposes to use the interactional context of Twitter threads, focusing on rumouroriented stance classification, where the objective is to identify support towards a rumour and an entire statement, rather than individual target concepts.…”

Section: Introductionmentioning

confidence: 99%

Multilingual Multi-Target Stance Recognition in Online Public Consultations

Barrière¹,

Balahur

2023

Mathematics

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Machine Learning is an interesting tool for stance recognition in a large-scale context, in terms of data size, but also regarding the topics and themes addressed or the languages employed by the participants. Public consultations of citizens using online participatory democracy platforms offer this kind of setting and are good use cases for automatic stance recognition systems. In this paper, we propose to use three datasets of public consultations, in order to train a model able to classify the stance of a citizen within a text, towards a proposal or a debate question. We studied stance detection in several contexts: using data from an online platform without interactions between users, using multilingual data from online debates that are in one language, and using data from online intra-multilingual debates, which can contain several languages inside the same unique debate discussion. We propose several baselines and methods in order to take advantage of the different available data, by comparing the results of models using out-of-dataset annotations, and binary or ternary annotations from the target dataset. We finally proposed a self-supervised learning method to take advantage of unlabelled data. We annotated both the datasets with ternary stance labels and made them available.

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Moral Stance Recognition and Polarity Classification from Twitter and Elicited Text

Cited by 9 publications

References 6 publications

BERTabaporu: Assessing a Genre-specific Language Model for Portuguese NLP

BERTabaporu: Assessing a Genre-specific Language Model for Portuguese NLP

Authorship attribution using author profiling classifiers

Multilingual Multi-Target Stance Recognition in Online Public Consultations

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