A Deep Learning Emotion Classification Framework for Low Resource Languages

-, Manisha, Clifford, William, McLaughlin, Eugene and Stynes, Paul (2023) A Deep Learning Emotion Classification Framework for Low Resource Languages. In: Big Data and Artificial Intelligence. Lecture Notes in Computer Science, 14418 . Springer, Cham, pp. 113-121.

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Abstract

Emotion classification from text is the process of identifying and classifying emotions expressed in textual data. Emotions can be feelings such as anger, joy, suspense, sadness and neutral. Developing a machine learning model to identify emotions in a low-resourced language with a limited set of linguistic resources and annotated corpora is a challenge. This research proposes a Deep Learning Emotion Classification Framework to identify and classify emotions in low-resourced languages such as Hindi. The proposed framework combines a classification model and a low resource optimization technique in a novel way. An annotated corpus of Hindi short stories consisting of 20,304 sentences is used to train the models for predicting five categories of emotions: anger, joy, suspense, sadness, and neutral talk. To resolve the class imbalance in the dataset SMOTE technique is applied. The optimal classification model is selected through experimentation that compares machine learning models and pre-trained models. Machine learning and deep learning models are SVM, Logistic Regression, Random Forest, CNN, BiLSTM, and CNN+BiLSTM. The pre-trained models, mBERT, IndicBERT, and a hybrid model, mBERT+BiLSTM. The models are evaluated based on macro average recall, macro average precision, and macro average F1 score. Results demonstrate that the hybrid model mBERT+BiLSTM out perform other models with a test accuracy of 57%.

Item Type:	Book Section
Uncontrolled Keywords:	Deep learning; Emotion classification; Low resource languages; Pre-trained model
Subjects:	P Language and Literature > PK Indo-Iranian P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing B Philosophy. Psychology. Religion > Psychology > Emotions Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	09 Dec 2023 11:25
Last Modified:	09 Dec 2023 12:28
URI:	https://norma.ncirl.ie/id/eprint/6880

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