Fuzzy-rough nearest neighbour approaches for emotion detection in tweets
- Author
- Olha Kaminska, Chris Cornelis (UGent) and Veronique Hoste (UGent)
- Organization
- 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.
- Keywords
- Fuzzy-rough nearest neighbour approach, Emotion detection, Natural language processing
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8754243
- MLA
- Kaminska, Olha, et al. “Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets.” ROUGH SETS (IJCRS 2021), edited by Sheela Ramanna et al., vol. 12872, Springer, 2021, pp. 231–46, doi:10.1007/978-3-030-87334-9_20.
- APA
- Kaminska, O., Cornelis, C., & Hoste, V. (2021). Fuzzy-rough nearest neighbour approaches for emotion detection in tweets. In S. Ramanna, C. Cornelis, & D. Ciucci (Eds.), ROUGH SETS (IJCRS 2021) (Vol. 12872, pp. 231–246). https://doi.org/10.1007/978-3-030-87334-9_20
- Chicago author-date
- Kaminska, Olha, Chris Cornelis, and Veronique Hoste. 2021. “Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets.” In ROUGH SETS (IJCRS 2021), edited by Sheela Ramanna, Chris Cornelis, and Davide Ciucci, 12872:231–46. Springer. https://doi.org/10.1007/978-3-030-87334-9_20.
- Chicago author-date (all authors)
- Kaminska, Olha, Chris Cornelis, and Veronique Hoste. 2021. “Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets.” In ROUGH SETS (IJCRS 2021), ed by. Sheela Ramanna, Chris Cornelis, and Davide Ciucci, 12872:231–246. Springer. doi:10.1007/978-3-030-87334-9_20.
- Vancouver
- 1.Kaminska O, Cornelis C, Hoste V. Fuzzy-rough nearest neighbour approaches for emotion detection in tweets. In: Ramanna S, Cornelis C, Ciucci D, editors. ROUGH SETS (IJCRS 2021). Springer; 2021. p. 231–46.
- IEEE
- [1]O. Kaminska, C. Cornelis, and V. Hoste, “Fuzzy-rough nearest neighbour approaches for emotion detection in tweets,” in ROUGH SETS (IJCRS 2021), Bratislava, SLOVAKIA, 2021, vol. 12872, pp. 231–246.
@inproceedings{8754243,
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.}},
author = {{Kaminska, Olha and Cornelis, Chris and Hoste, Veronique}},
booktitle = {{ROUGH SETS (IJCRS 2021)}},
editor = {{Ramanna, Sheela and Cornelis, Chris and Ciucci, Davide}},
isbn = {{9783030873332}},
issn = {{0302-9743}},
keywords = {{Fuzzy-rough nearest neighbour approach,Emotion detection,Natural language processing}},
language = {{eng}},
location = {{Bratislava, SLOVAKIA}},
pages = {{231--246}},
publisher = {{Springer}},
title = {{Fuzzy-rough nearest neighbour approaches for emotion detection in tweets}},
url = {{http://doi.org/10.1007/978-3-030-87334-9_20}},
volume = {{12872}},
year = {{2021}},
}
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