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Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets

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Rough Sets (IJCRS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12872))

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Abstract

Social media are an essential source of meaningful data that can be used in different tasks such as sentiment analysis and emotion recognition. Mostly, these tasks are solved with deep learning methods. Due to the fuzzy nature of textual data, we consider using classification methods based on fuzzy rough sets.

Specifically, we develop an approach for the SemEval-2018 emotion detection task, based on the fuzzy rough nearest neighbour (FRNN) classifier enhanced with ordered weighted average (OWA) operators. We use tuned ensembles of FRNN–OWA models based on different text embedding methods. Our results are competitive with the best SemEval solutions based on more complicated deep learning methods.

This work was supported by the Odysseus programme of the Research Foundation–Flanders (FWO).

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Notes

  1. 1.

    https://competitions.codalab.org/competitions/17751.

  2. 2.

    The source code: https://github.com/olha-kaminska/frnn_emotion_detection.

  3. 3.

    Competition results: https://competitions.codalab.org/competitions/17751#results.

  4. 4.

    https://en.wikipedia.org/wiki/List_of_emoticons.

  5. 5.

    https://pypi.org/project/emoji/.

  6. 6.

    https://gist.github.com/sebleier/554280.

  7. 7.

    https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM.

  8. 8.

    https://deepmoji.mit.edu/.

  9. 9.

    https://github.com/huggingface/torchMoji.

  10. 10.

    https://www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder.

  11. 11.

    https://github.com/dnanhkhoa/pytorch-pretrained-BERT/blob/master/examples/extract_features.py.

  12. 12.

    p refers to the number of elements in the OWA weight vector.

  13. 13.

    https://github.com/oulenz/fuzzy-rough-learn.

  14. 14.

    https://competitions.codalab.org/competitions/17751#learn_the_details-evaluation.

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Author information

Authors and Affiliations

  1. Computational Web Intelligence Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Olha Kaminska & Chris Cornelis

  2. LT3 Language and Translation Technology Team, Ghent University, Ghent, Belgium

    Veronique Hoste

Authors
  1. Olha Kaminska
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  2. Chris Cornelis
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  3. Veronique Hoste
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Corresponding author

Correspondence to Olha Kaminska .

Editor information

Editors and Affiliations

  1. University of Winnipeg, Winnipeg, MB, Canada

    Sheela Ramanna

  2. Ghent University, Gent, Belgium

    Chris Cornelis

  3. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

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Kaminska, O., Cornelis, C., Hoste, V. (2021). Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets. In: Ramanna, S., Cornelis, C., Ciucci, D. (eds) Rough Sets. IJCRS 2021. Lecture Notes in Computer Science(), vol 12872. Springer, Cham. https://doi.org/10.1007/978-3-030-87334-9_20

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  • DOI: https://doi.org/10.1007/978-3-030-87334-9_20

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