## 5 Conclusions  
In conclusion, the growing popularity and value of various cryptocurrencies, including the emergence of meme coins like Dogecoin and Shiba Inu, have been driven by a combination of technological innovation and marketing strategies. In particular, social media platforms and influential figures like [PERSON] have played key roles in shaping the cryptocurrency landscape. Our study has successfully identified the major factors influencing cryptocurrency price fluctuations, with a primary emphasis on social media data. By analyzing the correlation between tweet frequency, likes, retweets, and user popularity, we revealed the significant impact of social media on cryptocurrency prices. Additionally, we explored how high-cap cryptocurrencies can influence the broader market, especially meme coins, which are highly susceptible to external factors. Using a combination of different statistical analysis, text analysis, and deep learning techniques, we developed a methodology for predicting the price fluctuations of cryptocurrencies and suggesting the optimal moments for trading in order to maximize profits. In particular, we defined a trading recommendation algorithm that, exploiting price predictions provided by an LSTM model, leads to a total profit of 194% when transaction fees are not taken into account and of 117% when transaction fees are considered. Moreover, it proved highly effective in predicting the price trend of influential meme coins. Considering only such a category of coins, the algorithm resulted in a substantial average profit of 902% with fees and 1258% without fees. The proposed trading algorithm can serve as a powerful tool to optimize the trading strategies and maximize profits. Looking ahead, there is potential to further refine the algorithm by adopting a less risky capital management approach to better control \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Cryptocurrency** & **Acronym** & **Category** & **Profit \% with Fees** & **Profit \% without Fees** \\ \hline Bitcoin & BTC & HC & \(-14.91\) & \(+227.38\) \\ Ethereum & ETH & HC & \(+6.41\) & \(+16.35\) \\ Polygon & MATIC & HC & \(+164.55\) & \(+178.43\) \\ Polkadot & DOT & HC & \(-47.80\) & \(+28.13\) \\ Solana & SQL & HC & \(-33.56\) & \(-10.55\) \\ Cosmos & ATOM & HC & \(-42.27\) & \(+13.89\) \\ Stellar & XLM & HC & \(+40.01\) & \(+48.26\) \\ Avalanche & AVAX & HC & \(+23.26\) & \(+29.37\) \\ TRON & TRX & HC & \(0.00\) & \(0.00\) \\ Litecoin & LTC & HC & \(+10.58\) & \(+85.98\) \\ Conflux & CEX & SP & \(+112.46\) & \(+202.52\) \\ Stacks & STX & SP & \(+59.89\) & \(+109.21\) \\ Fantom & FTM & SP & \(-25.42\) & \(-22.88\) \\ Quant & QNT & SP & \(+74.17\) & \(+83.96\) \\ Loopring & LRC & SP & \(+3.73\) & \(+4.73\) \\ The Sandbox & SAND & SP & \(-80.04\) & \(-30.06\) \\ Gala & GALA & SP & \(+259.01\) & \(+297.28\) \\ Lido DAO & LDO & SP & \(-79.72\) & \(+85.95\) \\ Cronos & CRO & SP & \(-15.32\) & \(+5.68\) \\ Zilliqa & ZIL & SP & \(+38.22\) & \(+58.44\) \\ Chiliz & CHZ & SP & \(0.00\) & \(0.00\) \\ Neo & NEO & SP & \(+132.66\) & \(+240.80\) \\ VeThor Token & VTHO & SP & \(+26.57\) & \(+32.84\) \\ Bancor & BNT & SP & \(-7.44\) & \(-5.50\) \\ The Graph & GRT & SP & \(-25.37\) & \(-18.11\) \\ Dogecoin & DOGE & MCI & \(+27.68\) & \(+39.41\) \\ Shiba Inu & SHIB & MCI & \(+432.47\) & \(+771.74\) \\ Decentraland & MANA & MCI & \(+2247.29\) & \(+2962.72\) \\ \hline
**Mean profit for HC coins** & & & **+10.63** & **+61.72** \\
**Mean profit for SP coins** & & & **+31.56** & **+69.66** \\
**Mean profit for IM coins** & & & **+902.48** & **+1257.96** \\
**Overall mean profit** & & & **+117.40** & **+194.14** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results obtained on selected cryptocurrencies categorized as high capitalization (HC), solid project (SP), and influential meme coin (IM). losses. This involves mitigating the risks associated with using the entire capital in each financial operation and minimizing the negative impact of trading commissions.