Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

In this paper, we propose a multilevel feature representation method that combines word-level features, such as German morphology and slang, and sentence-level features, such as special symbols and English-translated sentiment information, and build a deep learning model for German sentiment classif...

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Autor principal:	Xiang Li
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/96bd341019f14d4f8ea5d9fc49c5a69d
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id	oai:doaj.org-article:96bd341019f14d4f8ea5d9fc49c5a69d
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spelling	oai:doaj.org-article:96bd341019f14d4f8ea5d9fc49c5a69d2021-11-29T00:56:21ZText Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism1939-012210.1155/2021/8309586https://doaj.org/article/96bd341019f14d4f8ea5d9fc49c5a69d2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8309586https://doaj.org/toc/1939-0122In this paper, we propose a multilevel feature representation method that combines word-level features, such as German morphology and slang, and sentence-level features, such as special symbols and English-translated sentiment information, and build a deep learning model for German sentiment classification based on the self-attentive mechanism, in order to address the characteristics of German social media texts that are colloquial, irregular, and diverse. Compared with the existing studies, this model not only has the most obvious improvement effect but also has better feature extraction and classification ability for German emotion.Xiang LiHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Xiang Li Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism
description	In this paper, we propose a multilevel feature representation method that combines word-level features, such as German morphology and slang, and sentence-level features, such as special symbols and English-translated sentiment information, and build a deep learning model for German sentiment classification based on the self-attentive mechanism, in order to address the characteristics of German social media texts that are colloquial, irregular, and diverse. Compared with the existing studies, this model not only has the most obvious improvement effect but also has better feature extraction and classification ability for German emotion.
format	article
author	Xiang Li
author_facet	Xiang Li
author_sort	Xiang Li
title	Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism
title_short	Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism
title_full	Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism
title_fullStr	Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism
title_full_unstemmed	Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism
title_sort	text sentiment analysis of german multilevel features based on self-attention mechanism
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/96bd341019f14d4f8ea5d9fc49c5a69d
work_keys_str_mv	AT xiangli textsentimentanalysisofgermanmultilevelfeaturesbasedonselfattentionmechanism
_version_	1718407721633447936

Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

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