Cargando…

Deep Learning-Based Text Emotion Analysis for Legal Anomie

Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the w...

Descripción completa

Detalles Bibliográficos
Autor principal: She, Botong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247634/
https://www.ncbi.nlm.nih.gov/pubmed/35783806
http://dx.doi.org/10.3389/fpsyg.2022.909157
_version_ 1784739202289631232
author She, Botong
author_facet She, Botong
author_sort She, Botong
collection PubMed
description Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences (such as adjectives, adverbs, nouns, etc.), which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory (ConvLSTM) network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis.
format Online
Article
Text
id pubmed-9247634
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-92476342022-07-02 Deep Learning-Based Text Emotion Analysis for Legal Anomie She, Botong Front Psychol Psychology Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences (such as adjectives, adverbs, nouns, etc.), which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory (ConvLSTM) network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis. Frontiers Media S.A. 2022-06-17 /pmc/articles/PMC9247634/ /pubmed/35783806 http://dx.doi.org/10.3389/fpsyg.2022.909157 Text en Copyright © 2022 She. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
She, Botong
Deep Learning-Based Text Emotion Analysis for Legal Anomie
title Deep Learning-Based Text Emotion Analysis for Legal Anomie
title_full Deep Learning-Based Text Emotion Analysis for Legal Anomie
title_fullStr Deep Learning-Based Text Emotion Analysis for Legal Anomie
title_full_unstemmed Deep Learning-Based Text Emotion Analysis for Legal Anomie
title_short Deep Learning-Based Text Emotion Analysis for Legal Anomie
title_sort deep learning-based text emotion analysis for legal anomie
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247634/
https://www.ncbi.nlm.nih.gov/pubmed/35783806
http://dx.doi.org/10.3389/fpsyg.2022.909157
work_keys_str_mv AT shebotong deeplearningbasedtextemotionanalysisforlegalanomie