Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions
Abstract Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problem...
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SpringerOpen
2024-10-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-024-01016-2 |
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| author | Jayaraman Kumarappan Elakkiya Rajasekar Subramaniyaswamy Vairavasundaram Ketan Kotecha Ambarish Kulkarni |
| author_facet | Jayaraman Kumarappan Elakkiya Rajasekar Subramaniyaswamy Vairavasundaram Ketan Kotecha Ambarish Kulkarni |
| author_sort | Jayaraman Kumarappan |
| collection | DOAJ |
| description | Abstract Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). Two data sources feed the model: specifically, NIFTY-50 Stock Market and real-time Twitter sentiment. Through Natural Language Processing (NLP), the raw data is pre-processed and key features are extracted before they are fused into a unified dataset using a cross-domain transformer, namely CDSFT, and then Circle-Inspired Optimization Algorithm (CIOA) selects the most important features from this dataset. This decreases the complexity of the model without losing essential information. Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. The proposed model Siagra-ConSA-HSOA achieved 99.9% accuracy and 99.8% recall in the testing stage, meaning that such a model performs better than the current approaches both in prediction accuracy and efficiency. Thus, this is a glimmer that the model shall be able to overcome some of the main problems with the current techniques used in predicting the behavior of the stock market. GitHub Repository: https://github.com/jramans2/Siamese-GCN-SplitAttention-Stock-Prediction.git |
| format | Article |
| id | doaj-art-a539dbbbb4014f479a44f9438944d1bb |
| institution | OA Journals |
| issn | 2196-1115 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-a539dbbbb4014f479a44f9438944d1bb2025-08-20T02:18:28ZengSpringerOpenJournal of Big Data2196-11152024-10-0111113410.1186/s40537-024-01016-2Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictionsJayaraman Kumarappan0Elakkiya Rajasekar1Subramaniyaswamy Vairavasundaram2Ketan Kotecha3Ambarish Kulkarni4Department of Computer Science, Birla Institute of Technology & Science, Pilani Dubai CampusDepartment of Computer Science, Birla Institute of Technology & Science, Pilani Dubai CampusSchool of Computer Science and Engineering, Vellore Institute of TechnologySymbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University)School of Engineering, Swinburne University of TechnologyAbstract Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). Two data sources feed the model: specifically, NIFTY-50 Stock Market and real-time Twitter sentiment. Through Natural Language Processing (NLP), the raw data is pre-processed and key features are extracted before they are fused into a unified dataset using a cross-domain transformer, namely CDSFT, and then Circle-Inspired Optimization Algorithm (CIOA) selects the most important features from this dataset. This decreases the complexity of the model without losing essential information. Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. The proposed model Siagra-ConSA-HSOA achieved 99.9% accuracy and 99.8% recall in the testing stage, meaning that such a model performs better than the current approaches both in prediction accuracy and efficiency. Thus, this is a glimmer that the model shall be able to overcome some of the main problems with the current techniques used in predicting the behavior of the stock market. GitHub Repository: https://github.com/jramans2/Siamese-GCN-SplitAttention-Stock-Prediction.githttps://doi.org/10.1186/s40537-024-01016-2Real time twitter dataCross domain Swin fusion Transformer (CDSFT)Circle-Inspired Optimization Algorithm (CIOA)Siamese Graph Convolutional Split-Attention Network (SGCSAN)Humboldt Squid Optimization Algorithm (HSOA) |
| spellingShingle | Jayaraman Kumarappan Elakkiya Rajasekar Subramaniyaswamy Vairavasundaram Ketan Kotecha Ambarish Kulkarni Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions Journal of Big Data Real time twitter data Cross domain Swin fusion Transformer (CDSFT) Circle-Inspired Optimization Algorithm (CIOA) Siamese Graph Convolutional Split-Attention Network (SGCSAN) Humboldt Squid Optimization Algorithm (HSOA) |
| title | Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions |
| title_full | Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions |
| title_fullStr | Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions |
| title_full_unstemmed | Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions |
| title_short | Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions |
| title_sort | siamese graph convolutional split attention network with nlp based social sentimental data for enhanced stock price predictions |
| topic | Real time twitter data Cross domain Swin fusion Transformer (CDSFT) Circle-Inspired Optimization Algorithm (CIOA) Siamese Graph Convolutional Split-Attention Network (SGCSAN) Humboldt Squid Optimization Algorithm (HSOA) |
| url | https://doi.org/10.1186/s40537-024-01016-2 |
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