Multi-label emotion classification of Urdu tweets

Noman Ashraf*, Lal Khan, Sabur Butt, Hsien Tsung Chang, Grigori Sidorov, Alexander Gelbukh

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

22 Scopus citations

Abstract

Urdu is a widely used language in South Asia and worldwide. While there are similar datasets available in English, we created the first multi-label emotion dataset consisting of 6,043 tweets and six basic emotions in the Urdu Nastalíq script. A multi-label (ML) classification approach was adopted to detect emotions from Urdu. The morphological and syntactic structure of Urdu makes it a challenging problem for multi-label emotion detection. In this paper, we build a set of baseline classifiers such as machine learning algorithms (Random forest (RF), Decision tree (J48), Sequential minimal optimization (SMO), AdaBoostM1, and Bagging), deep-learning algorithms (Convolutional Neural Networks (1D-CNN), Long short-term memory (LSTM), and LSTM with CNN features) and transformer-based baseline (BERT). We used a combination of text representations: stylometric-based features, pre-trained word embedding, word-based n-grams, and character-based n-grams. The paper highlights the annotation guidelines, dataset characteristics and insights into different methodologies used for Urdu based emotion classification. We present our best results using micro-averaged F1, macro-averaged F1, accuracy, Hamming loss (HL) and exact match (EM) for all tested methods.

Original languageEnglish
Article numbere896
JournalPeerJ Computer Science
Volume8
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© Copyright 2022 Ashraf et al

Keywords

  • Deep learning
  • Emotion classification in Urdu
  • Emotion detection
  • Machine learning
  • Multi-label emotion detection
  • Natural language processing

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