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Research Article Open access CC BY 4.0

Sarcasm Detection in Pidgin Tweets Using Machine Learning Techniques

Khadijat T Ladoja, Ruth T Afape

Asian Journal of Research in Computer Science · pp. 212–221 · Published 18 Mar 2024

10.9734/ajrcos/2024/v17i5450

Abstract

Detecting sarcasm in social media is of growing importance for applications such as monitoring, consumer feedback, and sentiment analysis. However, detecting sarcasm in Pidgin tweets poses unique challenges due to the blend of English and Pidgin languages, along with local cultural references. Existing models for sarcasm detection in English lack appropriate annotated data for Pidgin. This scarcity hinders the development of effective machine learning models. This research aims to address these challenges and create a model for accurate sarcasm detection in Pidgin tweets. Logistic Regression, XGBoost, Random Forest, and Vanilla Artificial Neural Network (ANN) classifiers were assessed, focusing on accuracy, precision, recall, and F1-score metrics on sarcasm data collected by curating and pre-processing a dataset of Nigerian Pidgin tweets. The XGBoost model demonstrated notable performance, attaining an accuracy of 85.78%, precision of 88.57%, recall of 94.44%, and F1-score of 91.41%. These outcomes underscored the model's prowess in discerning sarcastic and non-sarcastic expressions. By unfolding the intricacies of language in the Nigerian context, this research into sarcasm identification in Nigerian Pidgin text data introduced a comprehensive pipeline encompassing data curation, exploratory analysis, culturally tailored pre-processing, model training, evaluation, and prediction.

Sentiment analysis machine learning sarcasm text data pidgin dataset social media linguistic landscape

References (17)

  1. 1 A transformer-based approach to irony and sarcasm detection [DOI]
  2. 2 Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN [DOI]
  3. 3 Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal [DOI]
  4. 4 Sarcasm Detection with Self-matching Networks and Low-rank Bilinear Pooling [DOI]
  5. 5 KnowleNet: Knowledge fusion network for multimodal sarcasm detection [DOI]
  6. 6 BERT-LSTM model for sarcasm detection in code-mixed social media post [DOI]
  7. 7 Sarcasm detection using deep learning and ensemble learning [DOI]
  8. 8 Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model [DOI]
  9. 9 Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media [DOI]
  10. 10 Sarcasm detection in online comments using machine learning [DOI]
  11. 11 Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter [DOI]
  12. 12 Machine Learning based Sarcasm Detection on Twitter Data [DOI]
  13. 13 Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets [DOI]
  14. 14 Affective Representations for Sarcasm Detection [DOI]
  15. 15 Sarcasm detection in native English and English as a second language speakers. [DOI]
  16. 16 Computational Intelligence and Neuroscience [DOI]
  17. 17 Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification [DOI]

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