A swarm-optimization based fusion model of sentiment analysis for cryptocurrency price prediction
Abstract Social media has attracted society for decades due to its reciprocal and real-life nature. It influenced almost all societal entities, including governments, academics, industries, health, and finance. The Social Network generates unstructured information about brands, political issues, cry...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92563-y |
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| author | Dimple Tiwari Bhoopesh Singh Bhati Bharti Nagpal Amal Al-Rasheed Masresha Getahun Ben Othman Soufiene |
| author_facet | Dimple Tiwari Bhoopesh Singh Bhati Bharti Nagpal Amal Al-Rasheed Masresha Getahun Ben Othman Soufiene |
| author_sort | Dimple Tiwari |
| collection | DOAJ |
| description | Abstract Social media has attracted society for decades due to its reciprocal and real-life nature. It influenced almost all societal entities, including governments, academics, industries, health, and finance. The Social Network generates unstructured information about brands, political issues, cryptocurrencies, and global pandemics. The major challenge is translating this information into reliable consumer opinion as it contains jargon, abbreviations, and reference links with previous content. Several ensemble models have been introduced to mine the enormous noisy range on social platforms. Still, these need more predictability and are the less-generalized models for social sentiment analysis. Hence, an optimized stacked-Long Short-Term Memory (LSTM)-based sentiment analysis model is proposed for cryptocurrency price prediction. The model can find the relationships of latent contextual semantic and co-occurrence statistical features between phrases in a sentence. Additionally, the proposed model comprises multiple LSTM layers, and each layer is optimized with Particle Swarm Optimization (PSO) technique to learn based on the best hyperparameters. The model’s efficiency is measured in terms of confusion matrix, weighted f1-Score, weighted Precision, weighted Recall, training accuracy, and testing accuracy. Moreover, comparative results reveal that an optimized stacked LSTM outperformed. The objective of the proposed model is to introduce a benchmark sentiment analysis model for predicting cryptocurrency prices, which will be helpful for other societal sentiment predictions. A pretty significant thing for this presented model is that it can process multilingual and cross-platform social media data. This could be achieved by combining LSTMs with multilingual embeddings, fine-tuning, and effective preprocessing for providing accurate and robust sentiment analysis across diverse languages, platforms, and communication styles. |
| format | Article |
| id | doaj-art-8c26a86fa5e942f89bb53f5f6271dcce |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8c26a86fa5e942f89bb53f5f6271dcce2025-08-20T03:05:53ZengNature PortfolioScientific Reports2045-23222025-03-0115111810.1038/s41598-025-92563-yA swarm-optimization based fusion model of sentiment analysis for cryptocurrency price predictionDimple Tiwari0Bhoopesh Singh Bhati1Bharti Nagpal2Amal Al-Rasheed3Masresha Getahun4Ben Othman Soufiene5School of Engineering & Technology, Vivekananda Institute of Professional Studies - Technical CampusIndian Institute of Information TechnologyNSUT East Campus (Formerly Ambedkar Institute of Advanced Communication Technologies & Research)Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Science and Information Technology, College of Engineering and Technology, Kebri Dehar UniversityPRINCE Laboratory Research, ISITcom, University of SousseAbstract Social media has attracted society for decades due to its reciprocal and real-life nature. It influenced almost all societal entities, including governments, academics, industries, health, and finance. The Social Network generates unstructured information about brands, political issues, cryptocurrencies, and global pandemics. The major challenge is translating this information into reliable consumer opinion as it contains jargon, abbreviations, and reference links with previous content. Several ensemble models have been introduced to mine the enormous noisy range on social platforms. Still, these need more predictability and are the less-generalized models for social sentiment analysis. Hence, an optimized stacked-Long Short-Term Memory (LSTM)-based sentiment analysis model is proposed for cryptocurrency price prediction. The model can find the relationships of latent contextual semantic and co-occurrence statistical features between phrases in a sentence. Additionally, the proposed model comprises multiple LSTM layers, and each layer is optimized with Particle Swarm Optimization (PSO) technique to learn based on the best hyperparameters. The model’s efficiency is measured in terms of confusion matrix, weighted f1-Score, weighted Precision, weighted Recall, training accuracy, and testing accuracy. Moreover, comparative results reveal that an optimized stacked LSTM outperformed. The objective of the proposed model is to introduce a benchmark sentiment analysis model for predicting cryptocurrency prices, which will be helpful for other societal sentiment predictions. A pretty significant thing for this presented model is that it can process multilingual and cross-platform social media data. This could be achieved by combining LSTMs with multilingual embeddings, fine-tuning, and effective preprocessing for providing accurate and robust sentiment analysis across diverse languages, platforms, and communication styles.https://doi.org/10.1038/s41598-025-92563-yDeep LearningEnsemble LearningSwarm OptimizationSentiment AnalysisCryptocurrency |
| spellingShingle | Dimple Tiwari Bhoopesh Singh Bhati Bharti Nagpal Amal Al-Rasheed Masresha Getahun Ben Othman Soufiene A swarm-optimization based fusion model of sentiment analysis for cryptocurrency price prediction Scientific Reports Deep Learning Ensemble Learning Swarm Optimization Sentiment Analysis Cryptocurrency |
| title | A swarm-optimization based fusion model of sentiment analysis for cryptocurrency price prediction |
| title_full | A swarm-optimization based fusion model of sentiment analysis for cryptocurrency price prediction |
| title_fullStr | A swarm-optimization based fusion model of sentiment analysis for cryptocurrency price prediction |
| title_full_unstemmed | A swarm-optimization based fusion model of sentiment analysis for cryptocurrency price prediction |
| title_short | A swarm-optimization based fusion model of sentiment analysis for cryptocurrency price prediction |
| title_sort | swarm optimization based fusion model of sentiment analysis for cryptocurrency price prediction |
| topic | Deep Learning Ensemble Learning Swarm Optimization Sentiment Analysis Cryptocurrency |
| url | https://doi.org/10.1038/s41598-025-92563-y |
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