## 3 Proposed Methodology  
Our methodology aims to predict the price movements of cryptocurrencies through the examination of two large datasets: the first contains market data (e.g., prices and exchanged volumes) of a set of cryptocurrencies; and the second contains posts published on social media discussing such coins. These datasets have been combined and analyzed using different statistical analysis, text analysis, and deep learning techniques. The goal of the methodology is to define a trading algorithm capable of suggesting the moments in which to carry out sell-and-buy operations to maximize profits.  
As illustrated in Figure 1, the methodology is composed of different phases: data collection, data preprocessing, data enrichment, training machine learning models, and trading recommendation. In the following, we discuss each phase in detail, highlighting the crucial and strategic decisions that characterize our methodology.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{_Related Work_} & \multirow{2}{*}{**PT**} & \multirow{2}{*}{**TI**} & \multicolumn{3}{c}{**Social Media Data**} & \multicolumn{1}{c}{**Market Data**} & \multirow{2}{*}{**Deep**} & \multicolumn{1}{c}{**Type of**} \\ \cline{3-10}  & & & **PF** & **I** & **SA** & **BA** & **CC** & **TI** & **Learning** & **Coins** \\ \hline [PERSON] et al. [12] & x & - & - & - & - & - & x & x & x & HC, SP \\ [PERSON] et al. [10] & x & - & - & - & x & - & - & - & - & HC \\ [PERSON] et al. [16] & x & - & - & - & - & - & - & x & x & HC \\ [PERSON] et al. [28] & x & - & - & - & x & - & - & - & x & HC \\ [PERSON] et al. [25] & x & - & - & - & - & - & - & - & - & HC \\ [PERSON] et al. [9] & x & - & - & - & x & - & - & x & x & HC \\ [PERSON] [27] & x & - & x & x & x & - & - & - & x & HC, SP \\ [PERSON] et al. [15] & x & - & - & - & - & - & - & - & x & - \\ [PERSON] et al. [24] & x & x & - & - & - & - & x & x & - \\ [PERSON] et al. [13] & x & - & - & - & - & - & x & x & HC, SP \\ [PERSON] et al. [14] & x & - & - & - & - & - & - & x & x & HC, SP \\ [PERSON] et al. [18] & x & - & - & - & - & - & - & x & x & HC, SP \\ [PERSON] et al. [26] & - & - & x & x & x & x & - & - & - & HC \\ [PERSON] et al. [23] & x & - & - & - & - & - & - & - & x & HC \\ [PERSON] et al. [34] & x & - & - & - & - & - & x & - & x & HC \\ [PERSON] et al. [35] & x & - & - & - & - & - & - & x & x & HC, SP \\ [PERSON] et al. [33] & x & - & - & - & - & - & - & - & x & HC \\ [PERSON] et al. [21] & x & x & - & - & - & - & x & x & - & HC, SP, IM \\ [PERSON] et al. [17] & x & x & - & - & - & - & x & x & x & HC, SP, IM, VM \\ [PERSON] et al. [20] & x & x & - & - & - & - & x & x & x & \\ [PERSON] et al. [22] & x & x & - & - & - & - & - & - & - & HC, SP, IM \\ [PERSON] et al. [29] & x & x & - & - & - & - & - & x & - & HC, SP \\ [PERSON] et al. [19] & x & - & - & - & x & - & - & - & - & HC \\
**Our work** & x & x & x & x & x & x & x & x & x & **HC, SP, IM** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison among existing related works and their features.  
Figure 1: Execution flow of the proposed methodology.  
To deal with such large and heterogeneous data, we leveraged Apache Spark to process them efficiently. The use of such a framework for Big Data is widely adopted in the realm of Big Data analytics, enabling faster and more scalable data processing [36].