research-article

Context-aware Emotion Detection from Low-resource Urdu Language Using Deep Neural Network

Authors:

Muhammad Farrukh Bashir,

Abdul Rehman Javed,

Muhammad Umair Arshad,

Thippa Reddy Gadekallu,

Waseem Shahzad,

Mirza Omer BegAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 5

Article No.: 131, Pages 1 - 30

https://doi.org/10.1145/3528576

Published: 08 May 2023 Publication History

Abstract

Emotion detection (ED) plays a vital role in determining individual interest in any field. Humans use gestures, facial expressions, and voice pitch and choose words to describe their emotions. Significant work has been done to detect emotions from the textual data in English, French, Chinese, and other high-resource languages. However, emotion classification has not been well studied in low-resource languages (i.e., Urdu) due to the lack of labeled corpora. This article presents a publicly available Urdu Nastalique Emotions Dataset (UNED) of sentences and paragraphs annotated with different emotions and proposes a deep learning (DL)-based technique for classifying emotions in the UNED corpus. Our annotated UNED corpus has six emotions for both paragraphs and sentences. We perform extensive experimentation to evaluate the quality of the corpus and further classify it using machine learning and DL approaches. Experimental results show that the developed DL-based model performs better than generic machine learning approaches with an F1 score of 85% on the UNED sentence-based corpus and 50% on the UNED paragraph-based corpus.

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Index Terms

Context-aware Emotion Detection from Low-resource Urdu Language Using Deep Neural Network
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 5

May 2023

653 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3596451

Editor:
Imed Zitouni
Google, USA

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Publication History

Published: 08 May 2023

Online AM: 01 April 2022

Accepted: 23 March 2022

Revised: 28 December 2021

Received: 24 September 2021

Published in TALLIP Volume 22, Issue 5

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Willson Joseph CJaspher Willsie Kathrine GVimal SSumathi. SPelusi DBlanco Valencia XVerdú E(2024)Improved optimizer with deep learning model for emotion detection and classificationMathematical Biosciences and Engineering10.3934/mbe.202429021:7(6631-6657)Online publication date: 2024
https://doi.org/10.3934/mbe.2024290
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