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International Journal of Neutrosophic Science

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https://doi.org/10.54216/IJNS

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International Journal of Neutrosophic Science

Volume 25 , Issue 2 , PP: 84-92, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Integrating Neutrosophic Logic with Bi-directional LSTM Model for Predicting Stock Market Movements

S.S. Saravanaraj 1 , Vediyappan Govindan 2 * , Mana Donganont 3 , Broumi Said 4

  • 1 Department of Mathematics, Hindustan Institute of Technology, Chennai-603103, India - (saravanarajcibi2000@gmail.com)
  • 2 Department of Mathematics, Hindustan Institute of Technology, Chennai-603103, India - (govindoviya@gmail.com)
  • 3 School of Science, University of Phayao, Phayao 56000, Thailand - (mana.do@up.ac.th)
  • 4 Laboratory of Information Processing, Faculty of Science Ben M’Sik, University Hassan II, Casablanca, Morocoo; STIE team, Regional Center for the Professions of Education and Training (C.R.M.E.F), Casablanca-Settat, Morocco - (s.broumi@flbenmsik.ma)

    • Doi: https://doi.org/10.54216/IJNS.250208

    Received: February 07, 2024 Revised: April 29, 2024 Accepted: July 26, 2024
    Abstract

    In this paper, we present sentiment analysis on Twitter data by employing Neutrosophic Sentiment Analysis (NSA). NSA captures sentiments by considering three aspects: truth, falsehood, and indeterminacy, offering a more nuanced understanding of sentiment in tweets. To enhance this analysis, we integrate the results from Neutrosophic logic (NL) sentiment analysis into a Bi-directional Long Short-Term Memory (LSTM) model. This integration takes use of NL's capacity to manage uncertainty and indeterminacy in social media material, as well as the Bi-directional LSTM's capability to capture temporal relationships in sequential data. Our combined NL-Bidirectional LSTM technique attempts to increase the precision of forecasting, particularly when it comes to predicting stock market patterns based on Twitter sentiment. Through comprehensive evaluation, we demonstrate the effectiveness of this approach, highlighting its potential to address the inherent uncertainties and indeterminacies in social media data and thereby provide more reliable predictions for stock market movements.

    Keywords :

    Neutrosophic logic , Bi-directional LSTM , Stock market prediction , Sentimental analysis

    References

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    [2]       Sawhney, R., Agarwal, S., Wadhwa, A., & Shah, R. (2020, November). Deep attentive learning for stock movement prediction from social media text and company correlations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 8415-8426).

    [3]       Jing, N., Wu, Z., & Wang, H. (2021). A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert Systems with Applications, 178, 115019.

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    [6]       Srijiranon, K., Lertratanakham, Y., & Tanantong, T. (2022). A hybrid framework using PCA, EMD and LSTM methods for stock market price prediction with sentiment analysis. Applied Sciences, 12(21), 10823.

    [7]       Fazlija, B., & Harder, P. (2022). Using financial news sentiment for stock price direction prediction. Mathematics, 10(13), 2156.

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    [9]       Costola, M., Hinz, O., Nofer, M., & Pelizzon, L. (2023). Machine learning sentiment analysis, COVID-19 news and stock market reactions. Research in International Business and Finance, 64, 101881.

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    [12]    Hassan, M. H., Darwish, S. M., & Elkaffas, S. M. (2022). An efficient deadlock handling model based on neutrosophic logic: case study on real time healthcare database systems. IEEE Access, 10, 76607-76621.

    [13]    Abdel-Basset, M., Gunasekaran, M., Mohamed, M., & Smarandache, F. (2019). A novel method for solving the fully neutrosophic linear programming problems. Neural computing and applications, 31, 1595-1605.

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    Cite This Article As :
    Saravanaraj, S.S.. , Govindan, Vediyappan. , Donganont, Mana. , Said, Broumi. Integrating Neutrosophic Logic with Bi-directional LSTM Model for Predicting Stock Market Movements. International Journal of Neutrosophic Science, vol. , no. , 2025, pp. 84-92. DOI: https://doi.org/10.54216/IJNS.250208
    Saravanaraj, S. Govindan, V. Donganont, M. Said, B. (2025). Integrating Neutrosophic Logic with Bi-directional LSTM Model for Predicting Stock Market Movements. International Journal of Neutrosophic Science, (), 84-92. DOI: https://doi.org/10.54216/IJNS.250208
    Saravanaraj, S.S.. Govindan, Vediyappan. Donganont, Mana. Said, Broumi. Integrating Neutrosophic Logic with Bi-directional LSTM Model for Predicting Stock Market Movements. International Journal of Neutrosophic Science , no. (2025): 84-92. DOI: https://doi.org/10.54216/IJNS.250208
    Saravanaraj, S. , Govindan, V. , Donganont, M. , Said, B. (2025) . Integrating Neutrosophic Logic with Bi-directional LSTM Model for Predicting Stock Market Movements. International Journal of Neutrosophic Science , () , 84-92 . DOI: https://doi.org/10.54216/IJNS.250208
    Saravanaraj S. , Govindan V. , Donganont M. , Said B. [2025]. Integrating Neutrosophic Logic with Bi-directional LSTM Model for Predicting Stock Market Movements. International Journal of Neutrosophic Science. (): 84-92. DOI: https://doi.org/10.54216/IJNS.250208
    Saravanaraj, S. Govindan, V. Donganont, M. Said, B. "Integrating Neutrosophic Logic with Bi-directional LSTM Model for Predicting Stock Market Movements," International Journal of Neutrosophic Science, vol. , no. , pp. 84-92, 2025. DOI: https://doi.org/10.54216/IJNS.250208

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