簡易檢索 / 詳目顯示

回結果列表
研究生： 喻祥瑞
Nathaniel Xiang-Rui Yu
論文名稱： 透過情緒分析進行 LSTM 股票價格預測
LSTM Stock Price Prediction with Sentiment Analysis
指導教授： 呂永和
Yung-Ho Leu
口試委員: 楊維寧
Wei-Ning Yang
陳雲岫
Yun-Shiow Chen
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理系
Department of Information Management
論文出版年： 2023
畢業學年度： 112
語文別： 英文
論文頁數： 33
中文關鍵詞： 長短期記憶網絡 、股價預測 、情感分析 、果蠅優化算法 、金融市場
外文關鍵詞： LSTM, stock price prediction, fruit fly optimization algorithm, sentiment analysis, financial markets
相關次數： 點閱：73  下載：11 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • The focus of this thesis is on a stock price prediction method that combines Long Short-Term Memory (LSTM) networks with sentiment analysis derived from social media and news sources. This approach is further refined using the Fruit Fly Optimization Algorithm (FOA), which plays a crucial role in testing the sentiment model's predictions in real-world trading scenarios. The integration of FOA allows for a practical evaluation of the model's efficacy, aiming to enhance the accuracy and profitability of trading strategies in the financial market. This study not only provides a deeper insight into market dynamics through sentiment analysis but also demonstrates the value of using advanced computational methods to validate and optimize trading decisions in actual market conditions.

    Abstract I Acknowledgements II Contents III List of Figures V List of Tables VI Chapter 1: Introduction 1 1.1 Background 1 1.2 Problem Statement 2 1.3 Research Objectives 2 1.4 Significance 3 1.5 Scope and Limitations 3 Chapter 2: Literature Review 3 2.1 Stock Price Prediction: An Overview 3 2.2 LSTM Networks: Mechanism and Applications 5 2.3 Fruit Fly Optimization Algorithm (FOA) 6 Chapter 3: Theoretical Framework 7 3.1 Overview of LSTM Networks 7 3.2 Sentiment Analysis: Techniques and Approaches 9 3.3 Integration of LSTM and Sentiment Analysis 10 3.4 Fruit Fly Optimization Algorithm 11 Chapter 4: Methodology 12 4.1 Process Flow Diagram 12 4.2 Data Collection 13 4.3 Data Preprocessing 15 4.4 Model Development 18 4.5 Model Training Preliminaries 19 4.6 Evaluation Metrics 19 Chapter 5: Experiments and Results 20 5.1 Experimental Setup of LSTM 20 5.2 Results of LSTM with Sentiment Analysis 23 5.3 Experimental Setup of FOA 25 5.4 Results of FOA 27 Chapter 6: Discussion 28 6.1 Interpretation of Findings 28 6.2 Implications 30 6.3 Limitations and Challenges 30 Chapter 7: Conclusion and Future Work 31 7.1 Conclusion 31 7.2 Contributions 31 7.3 Recommendations 32 7.4 Future Work 33 References VI

    1. C.-H. Park and S. H. Irwin, "What do we know about the profitability of technical analysis?" J. Econ. Surveys, vol. 21, pp. 786-826, 2007.
    2. D. Shah, H. Isah, and F. Zulkernine, "Stock market analysis: A review and taxonomy of prediction techniques," Int. J. Financ. Stud., vol. 7, no. 2, p. 26, May 2019.
    3. C. Zhao et al., "Progress and Prospects of Data-Driven Stock Price Forecasting Research," Int. J. Cogn. Comput. Eng., Mar. 2023.
    4. K. Srijiranon, Y. Lertratanakham, and T. Tanantong, "A hybrid Framework Using PCA, EMD and LSTM methods for stock market price prediction with sentiment analysis," Appl. Sci., vol. 12, no. 21, p. 10823, Oct. 2022.
    5. M. Muhammad et al., "Transformer-based deep learning model for stock price prediction: A case study on Bangladesh stock market," Int. J. Comput. Intell. Appl., vol. 2023, p. 2350013, Apr. 2023.
    6. R. C. Staudemeyer and E. R. Morris, "Understanding LSTM--a tutorial into long short-term memory recurrent neural networks," arXiv preprint arXiv:1909.09586, Sep. 2019.
    7. A. Sherstinsky, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network," Phys. D, vol. 404, p. 132306, Mar. 2020.
    8. L. Yao and Y. Guan, "An improved LSTM structure for natural language processing," in Proc. IEEE Int. Conf. Safety Produce Informatization (IICSPI), Dec. 2018, pp. 565-569.
    9. S. Bharadwaj et al., "Resume Screening using NLP and LSTM," in Proc. Int. Conf. Inventive Comput. Technol. (ICICT), Jul. 2022, pp. 238-241.
    10. X. H. Le et al., "Application of long short-term memory (LSTM) neural network for flood forecasting," Water, vol. 11, no. 7, p. 1387, Jul. 2019.
    11. zhayunduo, "roberta-base-stocktwits-finetuned," Hugging Face. [Online]. Available: https://huggingface.co/zhayunduo/roberta-base-stocktwits-finetuned?text=aapl+boom
    12. W.-T. Pan, "A new fruit fly optimization algorithm: taking the financial distress model as an example," Knowl.-Based Syst., vol. 26, pp. 69-74, Feb. 2012.
    13. L. Wu et al., "A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems," Knowl.-Based Syst., vol. 144, pp. 153-173, Mar. 2018.
    14. W.-Y. Lin, "A novel 3D fruit fly optimization algorithm and its applications in economics," Neural Comput. Appl., vol. 27, pp. 1391-1413, Jul. 2016.

    QR CODE