Nearest neighbour approaches for Emotion Detection in Tweets

Abstract

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.

arXiv commentThe paper was presented at EACL 2021 during the WASSA workshop as a poster and published at ACL Anthology

References in corpus (4)

• A Survey of the State of Explainable AI for Natural Language Processing
• Crowdsourcing a Word-Emotion Association Lexicon
• Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification
• Explaining and Improving Model Behavior with k Nearest Neighbor Representations