Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions

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 © The Author(s) 2024.

Авторы
Kumarappan J. , Rajasekar E. , Vairavasundaram S. , Kotecha K. , Kulkarni A.
Журнал
Journal of Big Data
Издательство
Springer Nature
Номер выпуска
1
Язык
Английский
Статус
Опубликовано
Ссылка
Внешняя ссылка
DOI
10.1186/S40537-024-01016-2
Номер
154
Том
11
Год
2024
Организации
  • 1 Department of Computer Science, Birla Institute of Technology & amp
  • 2 Science, Pilani Dubai Campus, International Academic City, Dubai, 345055, United Arab Emirates
  • 3 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
  • 4 Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, India
  • 5 School of Engineering, Swinburne University of Technology, Hawthorn, Australia
  • 6 Peoples’ Friendship University, Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University), 6 Miklukho-Maklaya Str., Moscow, 117198, Russian Federation
Ключевые слова
Circle-Inspired Optimization Algorithm (CIOA); Cross domain Swin fusion Transformer (CDSFT); Humboldt Squid Optimization Algorithm (HSOA); Real time twitter data; Siamese Graph Convolutional Split-Attention Network (SGCSAN)
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