Modeling topic evolution in public opinion events: an unsupervised spatio-temporal graph attention approach
Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Xi [verfasserIn] Kong, Mingming [verfasserIn] Chen, Jiexin [verfasserIn] Wang, Xianjun [verfasserIn] Pei, Zheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 54(2024), 20 vom: 22. Juli, Seite 9706-9722 |
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| Übergeordnetes Werk: |
volume:54 ; year:2024 ; number:20 ; day:22 ; month:07 ; pages:9706-9722 |
| Links: |
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| DOI / URN: |
10.1007/s10489-024-05684-8 |
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| Katalog-ID: |
SPR056976070 |
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| 520 | |a Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period. | ||
| 650 | 4 | |a Unsupervised |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spatio-temporal graph attention |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Public opinion event |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kong, Mingming |e verfasserin |0 (orcid)0000-0003-2869-5441 |4 aut | |
| 700 | 1 | |a Chen, Jiexin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xianjun |e verfasserin |4 aut | |
| 700 | 1 | |a Pei, Zheng |e verfasserin |4 aut | |
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10.1007/s10489-024-05684-8 doi (DE-627)SPR056976070 (SPR)s10489-024-05684-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 30.20 bkl Wang, Xi verfasserin aut Modeling topic evolution in public opinion events: an unsupervised spatio-temporal graph attention approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period. Unsupervised (dpeaa)DE-He213 Spatio-temporal graph attention (dpeaa)DE-He213 Topic trend prediction (dpeaa)DE-He213 Public opinion event (dpeaa)DE-He213 Kong, Mingming verfasserin (orcid)0000-0003-2869-5441 aut Chen, Jiexin verfasserin aut Wang, Xianjun verfasserin aut Pei, Zheng verfasserin aut Enthalten in Applied intelligence Springer US, 1991 54(2024), 20 vom: 22. Juli, Seite 9706-9722 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:54 year:2024 number:20 day:22 month:07 pages:9706-9722 https://dx.doi.org/10.1007/s10489-024-05684-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 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_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ 30.20 VZ AR 54 2024 20 22 07 9706-9722 |
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10.1007/s10489-024-05684-8 doi (DE-627)SPR056976070 (SPR)s10489-024-05684-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 30.20 bkl Wang, Xi verfasserin aut Modeling topic evolution in public opinion events: an unsupervised spatio-temporal graph attention approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period. Unsupervised (dpeaa)DE-He213 Spatio-temporal graph attention (dpeaa)DE-He213 Topic trend prediction (dpeaa)DE-He213 Public opinion event (dpeaa)DE-He213 Kong, Mingming verfasserin (orcid)0000-0003-2869-5441 aut Chen, Jiexin verfasserin aut Wang, Xianjun verfasserin aut Pei, Zheng verfasserin aut Enthalten in Applied intelligence Springer US, 1991 54(2024), 20 vom: 22. Juli, Seite 9706-9722 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:54 year:2024 number:20 day:22 month:07 pages:9706-9722 https://dx.doi.org/10.1007/s10489-024-05684-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 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_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ 30.20 VZ AR 54 2024 20 22 07 9706-9722 |
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10.1007/s10489-024-05684-8 doi (DE-627)SPR056976070 (SPR)s10489-024-05684-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 30.20 bkl Wang, Xi verfasserin aut Modeling topic evolution in public opinion events: an unsupervised spatio-temporal graph attention approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period. Unsupervised (dpeaa)DE-He213 Spatio-temporal graph attention (dpeaa)DE-He213 Topic trend prediction (dpeaa)DE-He213 Public opinion event (dpeaa)DE-He213 Kong, Mingming verfasserin (orcid)0000-0003-2869-5441 aut Chen, Jiexin verfasserin aut Wang, Xianjun verfasserin aut Pei, Zheng verfasserin aut Enthalten in Applied intelligence Springer US, 1991 54(2024), 20 vom: 22. Juli, Seite 9706-9722 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:54 year:2024 number:20 day:22 month:07 pages:9706-9722 https://dx.doi.org/10.1007/s10489-024-05684-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 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_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ 30.20 VZ AR 54 2024 20 22 07 9706-9722 |
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10.1007/s10489-024-05684-8 doi (DE-627)SPR056976070 (SPR)s10489-024-05684-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 30.20 bkl Wang, Xi verfasserin aut Modeling topic evolution in public opinion events: an unsupervised spatio-temporal graph attention approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period. Unsupervised (dpeaa)DE-He213 Spatio-temporal graph attention (dpeaa)DE-He213 Topic trend prediction (dpeaa)DE-He213 Public opinion event (dpeaa)DE-He213 Kong, Mingming verfasserin (orcid)0000-0003-2869-5441 aut Chen, Jiexin verfasserin aut Wang, Xianjun verfasserin aut Pei, Zheng verfasserin aut Enthalten in Applied intelligence Springer US, 1991 54(2024), 20 vom: 22. Juli, Seite 9706-9722 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:54 year:2024 number:20 day:22 month:07 pages:9706-9722 https://dx.doi.org/10.1007/s10489-024-05684-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 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_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ 30.20 VZ AR 54 2024 20 22 07 9706-9722 |
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10.1007/s10489-024-05684-8 doi (DE-627)SPR056976070 (SPR)s10489-024-05684-8-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 30.20 bkl Wang, Xi verfasserin aut Modeling topic evolution in public opinion events: an unsupervised spatio-temporal graph attention approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period. Unsupervised (dpeaa)DE-He213 Spatio-temporal graph attention (dpeaa)DE-He213 Topic trend prediction (dpeaa)DE-He213 Public opinion event (dpeaa)DE-He213 Kong, Mingming verfasserin (orcid)0000-0003-2869-5441 aut Chen, Jiexin verfasserin aut Wang, Xianjun verfasserin aut Pei, Zheng verfasserin aut Enthalten in Applied intelligence Springer US, 1991 54(2024), 20 vom: 22. Juli, Seite 9706-9722 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:54 year:2024 number:20 day:22 month:07 pages:9706-9722 https://dx.doi.org/10.1007/s10489-024-05684-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 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_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ 30.20 VZ AR 54 2024 20 22 07 9706-9722 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR056976070</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240816064634.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240816s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-024-05684-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR056976070</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10489-024-05684-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">30.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Xi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modeling topic evolution in public opinion events: an unsupervised spatio-temporal graph attention approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unsupervised</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatio-temporal graph attention</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Topic trend prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Public opinion event</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kong, Mingming</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-2869-5441</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Jiexin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Xianjun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pei, Zheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">54(2024), 20 vom: 22. Juli, Seite 9706-9722</subfield><subfield code="w">(DE-627)271180919</subfield><subfield code="w">(DE-600)1479519-X</subfield><subfield code="x">1573-7497</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:54</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:20</subfield><subfield code="g">day:22</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:9706-9722</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10489-024-05684-8</subfield><subfield code="m">X:SPRINGER</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" 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Modeling topic evolution in public opinion events: an unsupervised spatio-temporal graph attention approach |
| abstract |
Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract With the widespread use of online social media, Public Opinion Events (POEs) quickly propagate on the Internet, generating a vast amount of textual data centered around various discussed topics. The development of POEs is closely linked to the evolution of these topics. However, in developing of POEs, the key challenges lie in estimating the duration of different topics, dealing with their dynamic natures, and quantifying topic evolution to predict the number of topics in the future. In this paper, we propose an Unsupervised Spatio-Temporal Graph Attention approach (USTGAT-TT) to tackle these challenges. First, we introduce a topic evolution periods generation method without human intervention. Initially, POEs data undergoes pre-processing to establish initial periods and extract keywords. According to the persistence and hotness of keywords, new periods are reconstructed and keywords are clustered by their similarity to form topics. Then we analyze three pieces of knowledge to further learn the evolution of topics, macro properties, micro properties and dynamic topic network graphs via topics co-occurrence relationship. Finally, we design a Spatio-Temporal Graph Attention topic trend prediction model (STGAT-TT) by taking the mutual effect of topics and temporal dependencies into account. At the same time, attention mechanism and average method are employed to obtain the contribution of topics and Long Short-Term Memory (LSTM) is used to predict the number of topics in the next period to study the state of POEs. Experiments on five POEs show that the effectiveness of the proposed approach. It can estimate the duration of topics to form periods and quantify their features to learn evolution and predict the number of topics in the next period. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Modeling topic evolution in public opinion events: an unsupervised spatio-temporal graph attention approach |
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https://dx.doi.org/10.1007/s10489-024-05684-8 |
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Kong, Mingming Chen, Jiexin Wang, Xianjun Pei, Zheng |
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Kong, Mingming Chen, Jiexin Wang, Xianjun Pei, Zheng |
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2024-08-16T04:48:09.553Z |
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| score |
7.4011316 |