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Emotion detection is an important task that can be applied to social mediadata to discover new knowledge. While the use of deep learning methods for thistask has been prevalent, they are black-box models, making their decisions hardto interpret for a human operator. Therefore, in this paper, we propose anapproach using weighted $k$ Nearest Neighbours (kNN), a simple, easy toimplement, and explainable machine learning model. These qualities can help toenhance results' reliability and guide error analysis. In particular, we applythe weighted kNN model to the shared emotion detection task in tweets fromSemEval-2018. Tweets are represented using different text embedding methods andemotion lexicon vocabulary scores, and classification is done by an ensemble ofweighted kNN models. Our best approaches obtain results competitive withstate-of-the-art solutions and open up a promising alternative path to neuralnetwork methods.

Nearest neighbour approaches for Emotion Detection in Tweets