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SS-PT: A Stance and Sentiment Data Set from Portuguese Quoted Tweets

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Computational Processing of the Portuguese Language (PROPOR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13208))

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Abstract

Our contribution presents the first ever stance and sentiment annotated corpus in Portuguese. Using data from Twitter, we annotated a data set with over 15,000 tweets. In building the corpus, we made both random and supervised searches to maximize balance. Using four annotators, we have classified each tweet on four categories: support, against, neutral or inconclusive. Furthermore, we have annotated each tweet for sentiment: positive, negative, neutral, sarcasm or inconclusive. Our annotators yield strong inter-annotator agreement. In addition, we test a baseline model using a pre-trained BERT model for Portuguese. Results suggest that our data set is in line with its counterparts in other languages.

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Notes

  1. 1.

    https://developer.twitter.com/en/docs/twitter-api.

  2. 2.

    We use Node2Vec’s package nodevectors (https://github.com/VHRanger/nodevectors) with the following parameters. \(n\_components = 10\), \(walklen = 50\), \(epochs = 20\), \(window = 10\), \(negative = 20\), \(iter = 10\), \(batch\_words = 128\).

  3. 3.

    We publish the data in agreement with Twitter API terms and conditions.

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Acknowledgements

This research was supported by the Portuguese Science and Technology Foundation (FCT), as part of the “Into the ‘Secret Garden’ of Portuguese Politics: Parliamentary Candidate Selection in Portugal, 1976-2015” project (PTDC/CPO-CPO/30296/2017), and the project with ref UIDB/50021/2020. We also thank BioData.pt for the computer resources.

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Authors and Affiliations

  1. INESC-ID, University of Lisbon, Lisbon, Portugal

    Miguel Won

  2. University of Lisbon, Lisbon, Portugal

    Jorge Fernandes

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  1. Miguel Won
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  2. Jorge Fernandes
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Correspondence to Miguel Won .

Editor information

Editors and Affiliations

  1. Universidade de Fortaleza, Fortaleza, Brazil

    Vládia Pinheiro

  2. CiTIUS - Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Pablo Gamallo

  3. Universidade Nova de Lisboa, Lisbon, Portugal

    Raquel Amaro

  4. University of Sheffield, Sheffield, UK

    Carolina Scarton

  5. INESC-ID, Lisbon, Portugal

    Fernando Batista

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

    Diego Silva

  7. University of Lisbon, Lisbon, Portugal

    Catarina Magro

  8. Sentimonitor, Porto Alegre, Brazil

    Hugo Pinto

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Won, M., Fernandes, J. (2022). SS-PT: A Stance and Sentiment Data Set from Portuguese Quoted Tweets. In: Pinheiro, V., et al. Computational Processing of the Portuguese Language. PROPOR 2022. Lecture Notes in Computer Science(), vol 13208. Springer, Cham. https://doi.org/10.1007/978-3-030-98305-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-98305-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98304-8

  • Online ISBN: 978-3-030-98305-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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