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BEmoC: A Corpus for Identifying Emotion in Bengali Texts

Emotion classification in text has growing interest among NLP experts due to the enormous availability of people’s emotions and its emergence on various Web 2.0 applications/services. Emotion classification in the Bengali texts is also gradually being considered as an important task for sports, e-co...

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Autores principales: Iqbal, MD. Asif, Das, Avishek, Sharif, Omar, Hoque, Mohammed Moshiul, Sarker, Iqbal H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762637/
https://www.ncbi.nlm.nih.gov/pubmed/35072102
http://dx.doi.org/10.1007/s42979-022-01028-w
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author Iqbal, MD. Asif
Das, Avishek
Sharif, Omar
Hoque, Mohammed Moshiul
Sarker, Iqbal H.
author_facet Iqbal, MD. Asif
Das, Avishek
Sharif, Omar
Hoque, Mohammed Moshiul
Sarker, Iqbal H.
author_sort Iqbal, MD. Asif
collection PubMed
description Emotion classification in text has growing interest among NLP experts due to the enormous availability of people’s emotions and its emergence on various Web 2.0 applications/services. Emotion classification in the Bengali texts is also gradually being considered as an important task for sports, e-commerce, entertainments, and security applications. However, It is a very critical task to develop an automatic emotion classification system for low-resource languages such as, Bengali. Scarcity of resources and deficiency of benchmark corpora make the task more complicated. Thus, the development of a benchmark corpus is the prerequisite to develop an emotion classifier for Bengali texts. This paper describes the development of an emotional corpus (hereafter called ‘BEmoC’) for classifying six emotions in Bengali texts. The corpus development process consists of four key steps: data crawling, pre-processing, labelling, and verification. A total of 7000 texts are labelled into six basic emotion categories such as anger, fear, surprise, sadness, joy, and disgust, respectively. Dataset evaluation with 0.969 Cohen’s κ score indicates the close agreement between the corpus annotators and the expert. The analysis of evaluation also represents that the distribution of emotion words obeys Zipf’s law. Moreover, the results of BEmoC analysis shown in terms of coding reliability, emotion density, and most frequent emotion words, respectively.
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spelling pubmed-87626372022-01-18 BEmoC: A Corpus for Identifying Emotion in Bengali Texts Iqbal, MD. Asif Das, Avishek Sharif, Omar Hoque, Mohammed Moshiul Sarker, Iqbal H. SN Comput Sci Original Research Emotion classification in text has growing interest among NLP experts due to the enormous availability of people’s emotions and its emergence on various Web 2.0 applications/services. Emotion classification in the Bengali texts is also gradually being considered as an important task for sports, e-commerce, entertainments, and security applications. However, It is a very critical task to develop an automatic emotion classification system for low-resource languages such as, Bengali. Scarcity of resources and deficiency of benchmark corpora make the task more complicated. Thus, the development of a benchmark corpus is the prerequisite to develop an emotion classifier for Bengali texts. This paper describes the development of an emotional corpus (hereafter called ‘BEmoC’) for classifying six emotions in Bengali texts. The corpus development process consists of four key steps: data crawling, pre-processing, labelling, and verification. A total of 7000 texts are labelled into six basic emotion categories such as anger, fear, surprise, sadness, joy, and disgust, respectively. Dataset evaluation with 0.969 Cohen’s κ score indicates the close agreement between the corpus annotators and the expert. The analysis of evaluation also represents that the distribution of emotion words obeys Zipf’s law. Moreover, the results of BEmoC analysis shown in terms of coding reliability, emotion density, and most frequent emotion words, respectively. Springer Singapore 2022-01-17 2022 /pmc/articles/PMC8762637/ /pubmed/35072102 http://dx.doi.org/10.1007/s42979-022-01028-w Text en © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Iqbal, MD. Asif
Das, Avishek
Sharif, Omar
Hoque, Mohammed Moshiul
Sarker, Iqbal H.
BEmoC: A Corpus for Identifying Emotion in Bengali Texts
title BEmoC: A Corpus for Identifying Emotion in Bengali Texts
title_full BEmoC: A Corpus for Identifying Emotion in Bengali Texts
title_fullStr BEmoC: A Corpus for Identifying Emotion in Bengali Texts
title_full_unstemmed BEmoC: A Corpus for Identifying Emotion in Bengali Texts
title_short BEmoC: A Corpus for Identifying Emotion in Bengali Texts
title_sort bemoc: a corpus for identifying emotion in bengali texts
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762637/
https://www.ncbi.nlm.nih.gov/pubmed/35072102
http://dx.doi.org/10.1007/s42979-022-01028-w
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