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