IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies
When emergencies are widely discussed and shared, it may lead to conflicting opinions and negative emotions among internet users. Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. Firs...
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| Veröffentlicht in: | PloS one 2023-10, Vol.18 (10), p.e0292677-e0292677 |
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| creator | Mu, Guangyu Liao, Zehan Li, Jiaxue Qin, Nini Yang, Ziye |
| description | When emergencies are widely discussed and shared, it may lead to conflicting opinions and negative emotions among internet users. Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. First, this model introduces an improved inertia weight and an adaptive variation operation to enhance the Particle Swarm Optimization (PSO) algorithm. Then, the improved PSO (IPSO) algorithm optimizes the parameters of the Long Short-Term Memory (LSTM) neural network. Finally, the IPSO-LSTM hybrid prediction model is constructed to forecast and analyze emergency public opinion dissemination trends. The experimental outcomes indicate that the IPSO-LSTM model surpasses others and has high prediction accuracy. In the four emergency predictions we select, the MAPE value of IPSO-LSTM is 74.27% better than that of BP, 33.96% better than that of LSTM, and 13.59% better than that of PSO-LSTM on average. This study aims to assist authorities in quickly identifying potential public opinion crises, developing effective strategies, and promoting sustainable and positive growth in the network environment. |
| doi_str_mv | 10.1371/journal.pone.0292677 |
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Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. First, this model introduces an improved inertia weight and an adaptive variation operation to enhance the Particle Swarm Optimization (PSO) algorithm. Then, the improved PSO (IPSO) algorithm optimizes the parameters of the Long Short-Term Memory (LSTM) neural network. Finally, the IPSO-LSTM hybrid prediction model is constructed to forecast and analyze emergency public opinion dissemination trends. The experimental outcomes indicate that the IPSO-LSTM model surpasses others and has high prediction accuracy. In the four emergency predictions we select, the MAPE value of IPSO-LSTM is 74.27% better than that of BP, 33.96% better than that of LSTM, and 13.59% better than that of PSO-LSTM on average. This study aims to assist authorities in quickly identifying potential public opinion crises, developing effective strategies, and promoting sustainable and positive growth in the network environment.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0292677</identifier><identifier>PMID: 37815983</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Biology and Life Sciences ; Computer and Information Sciences ; Emergencies ; Emergency management ; Emotions ; Evaluation ; Forecasting models ; Forecasts and trends ; Long short-term memory ; Management ; Mathematical models ; Mathematical optimization ; Modelling ; Neural networks ; Particle swarm optimization ; Physical Sciences ; Prediction models ; Public opinion ; Research and Analysis Methods ; Sentiment analysis ; Social Sciences ; Trends</subject><ispartof>PloS one, 2023-10, Vol.18 (10), p.e0292677-e0292677</ispartof><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Mu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Mu et al 2023 Mu et al</rights><rights>2023 Mu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. First, this model introduces an improved inertia weight and an adaptive variation operation to enhance the Particle Swarm Optimization (PSO) algorithm. Then, the improved PSO (IPSO) algorithm optimizes the parameters of the Long Short-Term Memory (LSTM) neural network. Finally, the IPSO-LSTM hybrid prediction model is constructed to forecast and analyze emergency public opinion dissemination trends. The experimental outcomes indicate that the IPSO-LSTM model surpasses others and has high prediction accuracy. In the four emergency predictions we select, the MAPE value of IPSO-LSTM is 74.27% better than that of BP, 33.96% better than that of LSTM, and 13.59% better than that of PSO-LSTM on average. This study aims to assist authorities in quickly identifying potential public opinion crises, developing effective strategies, and promoting sustainable and positive growth in the network environment.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Emergencies</subject><subject>Emergency management</subject><subject>Emotions</subject><subject>Evaluation</subject><subject>Forecasting models</subject><subject>Forecasts and trends</subject><subject>Long short-term memory</subject><subject>Management</subject><subject>Mathematical models</subject><subject>Mathematical optimization</subject><subject>Modelling</subject><subject>Neural networks</subject><subject>Particle swarm optimization</subject><subject>Physical Sciences</subject><subject>Prediction models</subject><subject>Public opinion</subject><subject>Research and Analysis Methods</subject><subject>Sentiment 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mu, Guangyu</au><au>Liao, Zehan</au><au>Li, Jiaxue</au><au>Qin, Nini</au><au>Yang, Ziye</au><au>Bacanin, Nebojsa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies</atitle><jtitle>PloS one</jtitle><date>2023-10-10</date><risdate>2023</risdate><volume>18</volume><issue>10</issue><spage>e0292677</spage><epage>e0292677</epage><pages>e0292677-e0292677</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>When emergencies are widely discussed and shared, it may lead to conflicting opinions and negative emotions among internet users. Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. First, this model introduces an improved inertia weight and an adaptive variation operation to enhance the Particle Swarm Optimization (PSO) algorithm. Then, the improved PSO (IPSO) algorithm optimizes the parameters of the Long Short-Term Memory (LSTM) neural network. Finally, the IPSO-LSTM hybrid prediction model is constructed to forecast and analyze emergency public opinion dissemination trends. The experimental outcomes indicate that the IPSO-LSTM model surpasses others and has high prediction accuracy. In the four emergency predictions we select, the MAPE value of IPSO-LSTM is 74.27% better than that of BP, 33.96% better than that of LSTM, and 13.59% better than that of PSO-LSTM on average. This study aims to assist authorities in quickly identifying potential public opinion crises, developing effective strategies, and promoting sustainable and positive growth in the network environment.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>37815983</pmid><doi>10.1371/journal.pone.0292677</doi><tpages>e0292677</tpages><orcidid>https://orcid.org/0000-0002-0884-8415</orcidid><orcidid>https://orcid.org/0000-0002-0199-3978</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analysis Biology and Life Sciences Computer and Information Sciences Emergencies Emergency management Emotions Evaluation Forecasting models Forecasts and trends Long short-term memory Management Mathematical models Mathematical optimization Modelling Neural networks Particle swarm optimization Physical Sciences Prediction models Public opinion Research and Analysis Methods Sentiment analysis Social Sciences Trends |
| title | IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies |
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