## 1 Introduction  
Bitcoin, introduced in 2009, is the first decentralized cryptocurrency that leverages blockchain technology to enable secure and anonymous online transactions. Known for its high volatility--it is about eight times more volatile than the stock market and 20 times more volatile than the US dollar [1]--Bitcoin has experienced significant price fluctuations. From a trading price of USD 0.06 in July 2010, it reached a peak of USD 69,000 in November 2021 before declining to USD 16,530 by the end of 2022. On 14 March 2024, it hit a record high of USD 75,830, indicating its incredible appreciation and increasing importance in the financial market [2]. Due to its capped supply of 21 million coins, Bitcoin is sometimes referred to as \"digital gold\" [3]. Its volatility and market capitalisation have made it a focal point in financial markets, driving the need for reliable forecasting techniques to navigate the associated risks and rewards.  
Recurrent Neural Networks (RNNs) are designed to process sequential data, making them suitable for tasks involving temporal patterns. Long Short-Term Memory (LSTM) networks, a specialized form of RNNs, excel in capturing long-term dependencies and are widely used in applications like language modelling, time-series forecasting, and sentiment analysis [4, 5]. Multivariate LSTM networks extend this by incorporating multiple input variables, enabling the modelling of complex relationships critical for tasks such as financial market prediction [6].  
There exists a range of studies, particularly early studies, that have utilised machine learning in market prediction using either historical pricing data or social media sentiment analysis but not a combination of the two. For instance, recent research such as [7] focusessolely on X (formerly known as Twitter) sentiment for Bitcoin price prediction. While sentiment analysis has shown potential, its limitations were noted as early as [PERSON] et al. [8], who relied on trends and sentiments alone, and were further corroborated by [PERSON] et al. [9], highlighting challenges.  
On the other hand, multivariate machine learning models, such as Transformers and LSTM networks, have demonstrated significant promise in capturing the temporal relationships between sentiment and price fluctuations while addressing these limitations [10; 11; 12; 13]. Although we acknowledge the advancements made in recent multivariate approaches, our research builds directly upon the foundational work of [PERSON] et al. [14], which utilized a univariate LSTM model. By extending their methodology to a multivariate framework and incorporating social media sentiment alongside historical Bitcoin pricing data, we aim to systematically examine the impact of these enhancements. Additionally, our study employs a similar dataset to enable a direct comparison of results, providing clarity on the improvements achievable through multivariate modelling.  
This research hence seeks to enhance prediction accuracy and contribute to more informed trading decisions in the volatile cryptocurrency market. Our study not only highlights the significance of integrating sentiment analysis with historical data but also positions itself as a critical evaluation of recent multivariate approaches within this domain.  
The remainder of this paper is organized as follows: Section 3 describes the data collection, preprocessing, and model development. Section 4 presents the experimental results. Section 5 discusses the findings and limitations of the study. Finally, Section 6 concludes the paper with directions for future research.