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Sentiment Gradient, An Enhancement to the Truth, Lies and Sarcasm Detection

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Advances in Artificial Intelligence – IBERAMIA 2022 (IBERAMIA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13788))

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Abstract

Information sharing on the Web has also led to the rise and spread of fake news. Considering that fake information is generally written to trigger stronger feelings from the readers than simple facts, sentiment analysis has been widely used to detect fake news. Nevertheless, sarcasm, irony, and even jokes use similar written styles, making the distinction between fake and fact harder to catch automatically. We propose a new fake news Classifier that considers a set of language attributes and the gradient of sentiments contained in a message. Sentiment analysis approaches are based on labelling news with a unique value that shrinks the entire message to a single feeling. We take a broader view of a message’s sentiment representation, trying to unravel the gradient of sentiments a message may bring. We tested our approach using two datasets containing texts written in Portuguese: a public one and another we created with more up-to-date news scrapped from the Internet. Although we believe our approach is general, we tested for the Portuguese language. Our results show that the sentiment gradient positively impacts the fake news classification performance with statistical significance. The F-Measure reached 94%, with our approach surpassing available ones (with a p-value less than 0.05 for our results).

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Notes

  1. 1.

    E-Farsas - http://www.e-farsas.com/secoes/falso-2.

  2. 2.

    Sensacionalista - https://www.sensacionalista.com.br/.

  3. 3.

    Folha de São Paulo - https://www.folha.uol.com.br/.

  4. 4.

    BeautifulSoup - https://www.crummy.com/software/BeautifulSoup/.

  5. 5.

    NLTK - https://www.nltk.org/.

  6. 6.

    Regular Expression - https://docs.python.org/3/library/re.html.

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Author information

Authors and Affiliations

  1. Federal University of the State of Rio de Janeiro - UNIRIO, Rio de Janeiro, Brazil

    Fernando Cardoso Durier da Silva, Ana Cristina Bicharra Garcia & Sean Wolfgand Matsui Siqueira

Authors
  1. Fernando Cardoso Durier da Silva
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  2. Ana Cristina Bicharra Garcia
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  3. Sean Wolfgand Matsui Siqueira
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Correspondence to Fernando Cardoso Durier da Silva .

Editor information

Editors and Affiliations

  1. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Ana Cristina Bicharra Garcia

  2. Fluminense Federal University, Niterói, Brazil

    Mariza Ferro

  3. University of Cartagena, Cartagena de Indias, Colombia

    Julio Cesar Rodríguez Ribón

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Cardoso Durier da Silva, F., Bicharra Garcia, A.C., Siqueira, S.W.M. (2022). Sentiment Gradient, An Enhancement to the Truth, Lies and Sarcasm Detection. In: Bicharra Garcia, A.C., Ferro, M., Rodríguez Ribón, J.C. (eds) Advances in Artificial Intelligence – IBERAMIA 2022. IBERAMIA 2022. Lecture Notes in Computer Science(), vol 13788. Springer, Cham. https://doi.org/10.1007/978-3-031-22419-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-22419-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22418-8

  • Online ISBN: 978-3-031-22419-5

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