## 6 Conclusions and Future Work  
This study systematically extended the univariate model of [PERSON] et al. [14] by developing multivariate LSTM architectures to evaluate the impact of integrating sentiment data on Bitcoin price prediction. We developed five LSTM-based models, including the Multi-LSTM-Sentiment (Section 3.3.1) and Bidirectional LSTM-Sentiment (Section 3.3.3) models, and evaluated their efficiency in predicting Bitcoin prices. By leveraging historical price and social media sentiment datasets, the tuned Multi-LSTM-Sentiment model (Section 3.3.2) emerged as the most reliable model, achieving the lowest error metrics (MAE of 0.00196 and RMSE of 0.00304). As future research, we recommend expanding the dataset to include more recent data or leveraging APIs like the X API to experiment with live data and assess the robustness of predictive models in real-time trading environments. It would also be interesting to integrate additional external features, such as economic indicators and news sentiment scores, within the dataset to enhance the predictive capabilities of hybrid models. Future work will also focus on implementing and benchmarking state-of-the-art (SOTA) models, such as the GRU and LSTM models from [19] or Transformer-based architectures mentioned in [11], to provide a more comprehensive evaluation of predictive performance. As future work, we aim to explore advanced regularization techniques, adaptive learning rate strategies, or multitask learning paradigms to enhance model stability under varied data splits and architectural changes. We would additionally like to compare the performance of our models with state-of-the-art models like the Transformer-based architectures mentioned in [11] to provide a more comprehensive evaluation of predictive performance. The high computational resources required for real-time sentiment analysis and training deep learning models pose a barrier, which future works can mitigate by exploring distributed computing or cloud-based solutions. Despite the above-mentioned limitations, we believe that the findings reaffirm the potential of LSTM-based architectures for cryptocurrency price prediction. This research contributes valuable insights into the role of sentiment-driven models in addressing the inherent volatility of the cryptocurrency market. Conceptualization, H.S.H., M.G. and S.S.; methodology, H.S.H. ; software, H.S.H. ; validation, H.S.H., M.G. and S.S.; formal analysis, H.S.H. ; investigation, H.S.H. ; resources, M.G. ; data curation, H.S.H. ; writing--original draft preparation, H.S.H. ; writing--journal paper preparation, M.G. ; writing--review and editing, S.S.; visualization, H.S.H. ; supervision, M.G. ; project administration, S.S.; guidance and submission planning, S.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** \\ \hline Multi-LSTM-Sentiment & 9.80247 & 0.00990 & 0.00934 \\ Tuned Multi-LSTM-Sentiment & 9.29112 & 0.00304 & 0.00196 \\ Bidirectional LSTM-Sentiment & 9.20827 & 0.00350 & 0.00253 \\ Hybrid LSTM-Volume & 2.96226 & 0.00544 & 0.00499 \\ Tuned Hybrid LSTM-Volume & 2.97542 & 0.00545 & 0.00420 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance metrics comparison for all models. Data Availability Statement: The data used in this study are publicly available under an open license and can be accessed at [[https://www.dataandsons.com/categories/markets/bitcoin-tweets-dataset-2017-to-2019](https://www.dataandsons.com/categories/markets/bitcoin-tweets-dataset-2017-to-2019)]([https://www.dataandsons.com/categories/markets/bitcoin-tweets-dataset-2017-to-2019](https://www.dataandsons.com/categories/markets/bitcoin-tweets-dataset-2017-to-2019)) 7 August 2024. Additional information is available upon request from the corresponding author. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: