1. Multi-Label Emotion Classification on Code-Mixed Text: Data and Methods
- Author
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Iqra Ameer, Grigori Sidorov, Helena Gomez-Adorno, and Rao Muhammad Adeel Nawab
- Subjects
Code-mixed text ,Roman Urdu and English ,deep learning ,classical machine learning ,multi-label emotion classification ,SMS messages ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The multi-label emotion classification task aims to identify all possible emotions in a written text that best represent the author’s mental state. In recent years, multi-label emotion classification attracted the attention of researchers due to its potential applications in e-learning, health care, marketing, etc. There is a need for standard benchmark corpora to develop and evaluate multi-label emotion classification methods. The majority of benchmark corpora were developed for the English language (monolingual corpora) using tweets. However, the multi-label emotion classification problem is not explored for code-mixed text, for example, English and Roman Urdu, although the code-mixed text is widely used in Facebook posts/comments, tweets, SMS messages, particularly by the South Asian community. For filling this gap, this study presents a large benchmark corpus for the multi-label emotion classification task, which comprises 11,914 code-mixed (English and Roman Urdu) SMS messages. Each code-mixed (English and Roman Urdu) SMS message manually annotated using a set of 12 emotions, including anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, trust, and neutral (no emotion). As a secondary contribution, we applied and compared state-of-the-art classical machine learning (content-based methods – three word n-gram features and eight character n-gram features), deep learning (CNN, RNN, Bi-RNN, GRU, Bi-GRU, LSTM, and Bi-LSTM), and transfer learning-based methods (BERT and XLNet) on our proposed corpus. After our extensive experimentation, the best results were obtained using state-of-the-art classical machine learning methods on word uni-gram (Micro Precision = 0.67, Micro Recall = 0.54, Micro F1 = 0.67) with a combination of OVR multi-label and SVC single-label machine learning algorithms. Our proposed corpus is free and publicly available for research purposes to foster research in an under-resourced language (Roman Urdu).
- Published
- 2022
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