Cargando…

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users' emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendat...

Descripción completa

Detalles Bibliográficos
Autores principales: Bhende, Manisha, Thakare, Anuradha, Pant, Bhasker, Singhal, Piyush, Shinde, Swati, Dugbakie, Betty Nokobi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356814/
https://www.ncbi.nlm.nih.gov/pubmed/35942448
http://dx.doi.org/10.1155/2022/5075277
_version_ 1784763600362012672
author Bhende, Manisha
Thakare, Anuradha
Pant, Bhasker
Singhal, Piyush
Shinde, Swati
Dugbakie, Betty Nokobi
author_facet Bhende, Manisha
Thakare, Anuradha
Pant, Bhasker
Singhal, Piyush
Shinde, Swati
Dugbakie, Betty Nokobi
author_sort Bhende, Manisha
collection PubMed
description With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users' emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively.
format Online
Article
Text
id pubmed-9356814
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-93568142022-08-07 Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions Bhende, Manisha Thakare, Anuradha Pant, Bhasker Singhal, Piyush Shinde, Swati Dugbakie, Betty Nokobi Comput Intell Neurosci Research Article With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users' emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively. Hindawi 2022-07-30 /pmc/articles/PMC9356814/ /pubmed/35942448 http://dx.doi.org/10.1155/2022/5075277 Text en Copyright © 2022 Manisha Bhende et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Bhende, Manisha
Thakare, Anuradha
Pant, Bhasker
Singhal, Piyush
Shinde, Swati
Dugbakie, Betty Nokobi
Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions
title Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions
title_full Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions
title_fullStr Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions
title_full_unstemmed Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions
title_short Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions
title_sort integrating multiclass light weighted bilstm model for classifying negative emotions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356814/
https://www.ncbi.nlm.nih.gov/pubmed/35942448
http://dx.doi.org/10.1155/2022/5075277
work_keys_str_mv AT bhendemanisha integratingmulticlasslightweightedbilstmmodelforclassifyingnegativeemotions
AT thakareanuradha integratingmulticlasslightweightedbilstmmodelforclassifyingnegativeemotions
AT pantbhasker integratingmulticlasslightweightedbilstmmodelforclassifyingnegativeemotions
AT singhalpiyush integratingmulticlasslightweightedbilstmmodelforclassifyingnegativeemotions
AT shindeswati integratingmulticlasslightweightedbilstmmodelforclassifyingnegativeemotions
AT dugbakiebettynokobi integratingmulticlasslightweightedbilstmmodelforclassifyingnegativeemotions