Papers › Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets
Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets
Olha Kaminska, Chris Cornelis, Veronique Hoste
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.
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