Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 8 Jul 2021.
Builds on
6 of 24 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 414
- 2020Deep Learning Based Text Classification: A Comprehensive Reviewcited by 140
- 2018SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweetscited by 7
- 2018Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classificationcited by 1
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Citing papers per year: 2023: 1.
1 paper.
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