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Brazilian Conference on Intelligent Systems

BRACIS 2020: Intelligent Systems pp 636–647Cite as

Twitter Moral Stance Classification Using Long Short-Term Memory Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12319))

Abstract

In Natural Language Processing, stance detection is the computational task of deciding whether a piece of text expresses a favourable or unfavourable attitude (or stance) towards a given topic. Stance detection may be divided into two subtasks: deciding whether a piece of text conveys any stance towards the target topic and, once we have established that the text does convey a stance, determining its polarity (e.g., favourable or unfavourable) towards the target. Both tasks - hereby called stance recognition and (stance) polarity classification - are the focus of the present work. Taking as a basis a corpus of 13.7k tweets in the Brazilian Portuguese language, and which conveys stances towards five moral issues (abortion legislation, death penalty, drug legalisation, lowering of criminal age, and racial quotas at universities), we compare a number of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) models for stance recognition and polarity classification. In doing so, the two tasks are addressed both independently and as a joint model. Results suggest that the use of BiLSTM models with attention mechanism outperform the alternatives under consideration, and pave the way for more comprehensive studies in this domain.

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Notes

  1. 1.

    Additional teams also participated in the unsupervised track of the competition, which is presently not discussed.

  2. 2.

    For instance, ‘death penalty for people who litter the streets’ does not convey a genuine stance towards the issue of death penalty.

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Acknowledgements

This work has been supported by the University of São Paulo PRP grant nr. 668/2018.

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Authors and Affiliations

  1. University of São Paulo (EACH-USP), Av Arlindo Bettio 1000, São Paulo, Brazil

    Matheus Camasmie Pavan, Wesley Ramos dos Santos & Ivandré Paraboni

Authors
  1. Matheus Camasmie Pavan
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  2. Wesley Ramos dos Santos
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  3. Ivandré Paraboni
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Corresponding author

Correspondence to Ivandré Paraboni .

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Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Ricardo Cerri

  2. Federal University of ABC, Santo Andre, Brazil

    Ronaldo C. Prati

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Pavan, M.C., dos Santos, W.R., Paraboni, I. (2020). Twitter Moral Stance Classification Using Long Short-Term Memory Networks. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_45

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  • DOI: https://doi.org/10.1007/978-3-030-61377-8_45

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  • Print ISBN: 978-3-030-61376-1

  • Online ISBN: 978-3-030-61377-8

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