Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets

COVID-19 has claimed several human lives to this date. People are dying not only because of physical infection of the virus but also because of mental illness, which is linked to people’s sentiments and psychologies. People’s written texts/posts scattered on the web could help understand their psych...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	C. Sitaula, A. Basnet, A. Mainali, T. B. Shahi
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/4fabf0720f4c4f079d1d4222ab61633b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4fabf0720f4c4f079d1d4222ab61633b
record_format	dspace
spelling	oai:doaj.org-article:4fabf0720f4c4f079d1d4222ab61633b2021-11-15T01:19:10ZDeep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets1687-527310.1155/2021/2158184https://doaj.org/article/4fabf0720f4c4f079d1d4222ab61633b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2158184https://doaj.org/toc/1687-5273COVID-19 has claimed several human lives to this date. People are dying not only because of physical infection of the virus but also because of mental illness, which is linked to people’s sentiments and psychologies. People’s written texts/posts scattered on the web could help understand their psychology and the state they are in during this pandemic. In this paper, we analyze people’s sentiment based on the classification of tweets collected from the social media platform, Twitter, in Nepal. For this, we, first, propose to use three different feature extraction methods—fastText-based (ft), domain-specific (ds), and domain-agnostic (da)—for the representation of tweets. Among these three methods, two methods (“ds” and “da”) are the novel methods used in this study. Second, we propose three different convolution neural networks (CNNs) to implement the proposed features. Last, we ensemble such three CNNs models using ensemble CNN, which works in an end-to-end manner, to achieve the end results. For the evaluation of the proposed feature extraction methods and CNN models, we prepare a Nepali Twitter sentiment dataset, called NepCOV19Tweets, with 3 classes (positive, neutral, and negative). The experimental results on such dataset show that our proposed feature extraction methods possess the discriminating characteristics for the sentiment classification. Moreover, the proposed CNN models impart robust and stable performance on the proposed features. Also, our dataset can be used as a benchmark to study the COVID-19-related sentiment analysis in the Nepali language.C. SitaulaA. BasnetA. MainaliT. B. ShahiHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 C. Sitaula A. Basnet A. Mainali T. B. Shahi Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets
description	COVID-19 has claimed several human lives to this date. People are dying not only because of physical infection of the virus but also because of mental illness, which is linked to people’s sentiments and psychologies. People’s written texts/posts scattered on the web could help understand their psychology and the state they are in during this pandemic. In this paper, we analyze people’s sentiment based on the classification of tweets collected from the social media platform, Twitter, in Nepal. For this, we, first, propose to use three different feature extraction methods—fastText-based (ft), domain-specific (ds), and domain-agnostic (da)—for the representation of tweets. Among these three methods, two methods (“ds” and “da”) are the novel methods used in this study. Second, we propose three different convolution neural networks (CNNs) to implement the proposed features. Last, we ensemble such three CNNs models using ensemble CNN, which works in an end-to-end manner, to achieve the end results. For the evaluation of the proposed feature extraction methods and CNN models, we prepare a Nepali Twitter sentiment dataset, called NepCOV19Tweets, with 3 classes (positive, neutral, and negative). The experimental results on such dataset show that our proposed feature extraction methods possess the discriminating characteristics for the sentiment classification. Moreover, the proposed CNN models impart robust and stable performance on the proposed features. Also, our dataset can be used as a benchmark to study the COVID-19-related sentiment analysis in the Nepali language.
format	article
author	C. Sitaula A. Basnet A. Mainali T. B. Shahi
author_facet	C. Sitaula A. Basnet A. Mainali T. B. Shahi
author_sort	C. Sitaula
title	Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets
title_short	Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets
title_full	Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets
title_fullStr	Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets
title_full_unstemmed	Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets
title_sort	deep learning-based methods for sentiment analysis on nepali covid-19-related tweets
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/4fabf0720f4c4f079d1d4222ab61633b
work_keys_str_mv	AT csitaula deeplearningbasedmethodsforsentimentanalysisonnepalicovid19relatedtweets AT abasnet deeplearningbasedmethodsforsentimentanalysisonnepalicovid19relatedtweets AT amainali deeplearningbasedmethodsforsentimentanalysisonnepalicovid19relatedtweets AT tbshahi deeplearningbasedmethodsforsentimentanalysisonnepalicovid19relatedtweets
_version_	1718428980987559936

Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets

Ejemplares similares