Nearest neighbour approaches for Emotion Detection in Tweets

Code for the paper written by Olha Kaminska, Chris Cornelis, and Veronique Hoste presented at EACL 2021 during WASSA workshop as a poster.

This paper uses data from SemEval-2018 Task 1: Affect in Tweets competition, where we participated in Task 2a "EI-oc" (emotion intensity ordered classification) for English tweets.

Repository Overview

• The code directory contains .py files with different functions:
  • preprocessing.py - functions for data uploading and preperation;
  • embeddings_and_lexicons.py - functions for tweets embeddings with different methods and lexicons;
  • wknn_eval.py - functions for wkNN approach and cross-validation evaluation.
• The data directory contains README_data_download.md file with instruction on uploading necessary dataset files that should be saved in the data folder.
• The lexica directory contains README_lexicons_download.md file with instruction on uploading necessary lexicons files that should be saved in the lexica folder.
• The model directory contains README_model_download.md file with instruction on uploading necessary models that should be saved in the model folder.
• The file Example.ipynb provides an overview of all function and their usage on the example of Anger dataset. It is built as a pipeline described in the paper with corresponded results.
• The file requirements.txt contains the list of all necessary packages and versions used with the Python 3.7.4 environment.
• The file WASSA2021_poster_Olha_Kaminska.pdf contains a poster that was presented for this paper at WASSA 2021.

ACL link

https://www.aclweb.org/anthology/2021.wassa-1.22/

Arxiv link

https://arxiv.org/abs/2107.05394

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.

BibTeX citation:

@inproceedings{kaminska2021nearest, title={Nearest neighbour approaches for Emotion Detection in Tweets}, author={Kaminska, Olha and Cornelis, Chris and Hoste, Veronique}, booktitle={Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis}, pages={203--212}, year={2021} }