Multi-label emotion classification of Urdu tweets

• Published in: PeerJ. Computer science Vol. 8; p. e896
• Main Authors: Ashraf, Noman, Khan, Lal, Butt, Sabur, Chang, Hsien-Tsung, Sidorov, Grigori, Gelbukh, Alexander
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 22.04.2022; PeerJ, Inc; PeerJ Inc
• Subjects: Algorithms; Analysis; Annotations; Artificial neural networks; Classification; Computational Linguistics; Data mining; Data Mining and Machine Learning; Data Science; Datasets; Decision trees; Deep learning; Emotion classification in Urdu; Emotion detection; Emotion recognition; Emotions; Language processing; Machine learning; Multi-label emotion detection; Natural language interfaces; Natural language processing; Neural networks; Optimization; Sentiment analysis; Social networks; United Kingdom; Deep learning; Multi-label emotion detection; Emotion classification in Urdu; Natural language processing; Emotion detection; Machine learning
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.896

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.