Multi-label emotion classification of Urdu tweets

PeerJ Computer Science

Noman Ashraf¹  Sabur Butt¹  Grigori Sidorov¹  Alexander Gelbukh¹  Lal Khan²  Hsien-Tsung Chang² 

[1] CIC, Instituto Politécnico Nacional, Mexico City, Mexico;Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan;

关键词: Emotion detection;    Emotion classification in Urdu;    Multi-label emotion detection;    Machine learning;    Deep learning;    Natural language processing;   

DOI  :  10.7717/peerj-cs.896

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown