Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing

Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multip...
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Autor*in:	Chu Liang [verfasserIn] Jiajie Xu [verfasserIn] Jie Zhao [verfasserIn] Ying Chen [verfasserIn] Jiwei Huang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Computational Intelligence and Neuroscience - Hindawi Limited, 2007, (2022)
Übergeordnetes Werk:	year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1155/2022/2241310

Katalog-ID:	DOAJ026819554

Internformat


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520			\|a Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model.
653		0	\|a Computer applications to medicine. Medical informatics
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spelling	10.1155/2022/2241310 doi (DE-627)DOAJ026819554 (DE-599)DOAJbeb572fe7f234296b2bd78854a5f4a0d DE-627 ger DE-627 rakwb eng R858-859.7 RC321-571 Chu Liang verfasserin aut Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model. Computer applications to medicine. Medical informatics Neurosciences. Biological psychiatry. Neuropsychiatry Jiajie Xu verfasserin aut Jie Zhao verfasserin aut Ying Chen verfasserin aut Jiwei Huang verfasserin aut In Computational Intelligence and Neuroscience Hindawi Limited, 2007 (2022) (DE-627)545783720 (DE-600)2388208-6 16875265 nnns year:2022 https://doi.org/10.1155/2022/2241310 kostenfrei https://doaj.org/article/beb572fe7f234296b2bd78854a5f4a0d kostenfrei http://dx.doi.org/10.1155/2022/2241310 kostenfrei https://doaj.org/toc/1687-5273 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
allfields_unstemmed	10.1155/2022/2241310 doi (DE-627)DOAJ026819554 (DE-599)DOAJbeb572fe7f234296b2bd78854a5f4a0d DE-627 ger DE-627 rakwb eng R858-859.7 RC321-571 Chu Liang verfasserin aut Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model. Computer applications to medicine. Medical informatics Neurosciences. Biological psychiatry. Neuropsychiatry Jiajie Xu verfasserin aut Jie Zhao verfasserin aut Ying Chen verfasserin aut Jiwei Huang verfasserin aut In Computational Intelligence and Neuroscience Hindawi Limited, 2007 (2022) (DE-627)545783720 (DE-600)2388208-6 16875265 nnns year:2022 https://doi.org/10.1155/2022/2241310 kostenfrei https://doaj.org/article/beb572fe7f234296b2bd78854a5f4a0d kostenfrei http://dx.doi.org/10.1155/2022/2241310 kostenfrei https://doaj.org/toc/1687-5273 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
allfieldsGer	10.1155/2022/2241310 doi (DE-627)DOAJ026819554 (DE-599)DOAJbeb572fe7f234296b2bd78854a5f4a0d DE-627 ger DE-627 rakwb eng R858-859.7 RC321-571 Chu Liang verfasserin aut Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model. Computer applications to medicine. Medical informatics Neurosciences. Biological psychiatry. Neuropsychiatry Jiajie Xu verfasserin aut Jie Zhao verfasserin aut Ying Chen verfasserin aut Jiwei Huang verfasserin aut In Computational Intelligence and Neuroscience Hindawi Limited, 2007 (2022) (DE-627)545783720 (DE-600)2388208-6 16875265 nnns year:2022 https://doi.org/10.1155/2022/2241310 kostenfrei https://doaj.org/article/beb572fe7f234296b2bd78854a5f4a0d kostenfrei http://dx.doi.org/10.1155/2022/2241310 kostenfrei https://doaj.org/toc/1687-5273 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
allfieldsSound	10.1155/2022/2241310 doi (DE-627)DOAJ026819554 (DE-599)DOAJbeb572fe7f234296b2bd78854a5f4a0d DE-627 ger DE-627 rakwb eng R858-859.7 RC321-571 Chu Liang verfasserin aut Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model. Computer applications to medicine. Medical informatics Neurosciences. Biological psychiatry. Neuropsychiatry Jiajie Xu verfasserin aut Jie Zhao verfasserin aut Ying Chen verfasserin aut Jiwei Huang verfasserin aut In Computational Intelligence and Neuroscience Hindawi Limited, 2007 (2022) (DE-627)545783720 (DE-600)2388208-6 16875265 nnns year:2022 https://doi.org/10.1155/2022/2241310 kostenfrei https://doaj.org/article/beb572fe7f234296b2bd78854a5f4a0d kostenfrei http://dx.doi.org/10.1155/2022/2241310 kostenfrei https://doaj.org/toc/1687-5273 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
language	English
source	In Computational Intelligence and Neuroscience (2022) year:2022
sourceStr	In Computational Intelligence and Neuroscience (2022) year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computer applications to medicine. Medical informatics Neurosciences. Biological psychiatry. Neuropsychiatry
isfreeaccess_bool	true
container_title	Computational Intelligence and Neuroscience
authorswithroles_txt_mv	Chu Liang @@aut@@ Jiajie Xu @@aut@@ Jie Zhao @@aut@@ Ying Chen @@aut@@ Jiwei Huang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	545783720
id	DOAJ026819554
language_de	englisch
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callnumber-first	R - Medicine
author	Chu Liang
spellingShingle	Chu Liang misc R858-859.7 misc RC321-571 misc Computer applications to medicine. Medical informatics misc Neurosciences. Biological psychiatry. Neuropsychiatry Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing
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topic_title	R858-859.7 RC321-571 Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing
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title	Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing
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author_browse	Chu Liang Jiajie Xu Jie Zhao Ying Chen Jiwei Huang
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title_sort	deep learning-based construction and processing of multimodal corpus for iot devices in mobile edge computing
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title_auth	Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing
abstract	Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model.
abstractGer	Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model.
abstract_unstemmed	Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model.
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title_short	Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing
url	https://doi.org/10.1155/2022/2241310 https://doaj.org/article/beb572fe7f234296b2bd78854a5f4a0d http://dx.doi.org/10.1155/2022/2241310 https://doaj.org/toc/1687-5273
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up_date	2024-07-03T23:06:46.192Z
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