An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets
The Internet’s development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public’s demands and respon...
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MDPI AG
2024-09-01
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| Series: | Biomimetics |
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| author | Guangyu Mu Jiaxue Li Xiurong Li Chuanzhi Chen Xiaoqing Ju Jiaxiu Dai |
| author_facet | Guangyu Mu Jiaxue Li Xiurong Li Chuanzhi Chen Xiaoqing Ju Jiaxiu Dai |
| author_sort | Guangyu Mu |
| collection | DOAJ |
| description | The Internet’s development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public’s demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian–Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network—Bidirectional Long Short-Term Memory (CNN-BiLSTM) model’s hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters. |
| format | Article |
| id | doaj-art-ff2ee253da2a40a3bf6f9c3708b1b37a |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Biomimetics |
| spelling | doaj-art-ff2ee253da2a40a3bf6f9c3708b1b37a2025-08-20T01:56:05ZengMDPI AGBiomimetics2313-76732024-09-019953310.3390/biomimetics9090533An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster TweetsGuangyu Mu0Jiaxue Li1Xiurong Li2Chuanzhi Chen3Xiaoqing Ju4Jiaxiu Dai5School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, ChinaThe Internet’s development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public’s demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian–Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network—Bidirectional Long Short-Term Memory (CNN-BiLSTM) model’s hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters.https://www.mdpi.com/2313-7673/9/9/533DBO algorithmdeep learningsocial mediasentiment analysisnatural disaster tweetsemergency management |
| spellingShingle | Guangyu Mu Jiaxue Li Xiurong Li Chuanzhi Chen Xiaoqing Ju Jiaxiu Dai An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets Biomimetics DBO algorithm deep learning social media sentiment analysis natural disaster tweets emergency management |
| title | An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets |
| title_full | An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets |
| title_fullStr | An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets |
| title_full_unstemmed | An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets |
| title_short | An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets |
| title_sort | enhanced idbo cnn bilstm model for sentiment analysis of natural disaster tweets |
| topic | DBO algorithm deep learning social media sentiment analysis natural disaster tweets emergency management |
| url | https://www.mdpi.com/2313-7673/9/9/533 |
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