ArticlePeerJ. Computer science2022
Multi-label emotion classification of Urdu tweets.
Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Heart disease prediction using hybrid TabNet architecture with stacked ensemble learning.Frontiers in physiology · 2025Article
- Evolving techniques in sentiment analysis: a comprehensive review.PeerJ. Computer science · 2025Article
- Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.Scientific reports · 2024Article
- A model for identifying potentially inappropriate medication used in older people with dementia: a machine learning study.International journal of clinical pharmacy · 2024Article
- Migraine headache (MH) classification using machine learning methods with data augmentation.Scientific reports · 2024Article
- Terrorism group prediction using feature combination and BiGRU with self-attention mechanism.PeerJ. Computer science · 2024Article
- Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study.Journal of clinical medicine · 2023Article
- Sentiment analysis of vegan related tweets using mutual information for feature selection.PeerJ. Computer science · 2022Article
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6 authors.
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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.
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