In the following text, change it to use advanced vocabulary but do not overuse it. Make sure to use proper grammar and spell check thoroughly. Show expertise in the subject provided, but do not add any extra information. Try to keep your response at the same length of words as the original. Use the same language as the original text. ONLY return the modified text and nothing else.



\begin{abstract}



    In this study, we explore the relationship between sentiment analysis of Reddit comments and Bitcoin price prediction. Reddit, with its diverse subreddits dedicated to Bitcoin discussions, provides a rich source of sentiment data. We collected posts and comments using specific keywords, and integrated historical Bitcoin price, trading volume, and sentiment data from June to December 2024 to forecast Bitcoin's future price. Sentiment analysis was performed using the VADER tool, and these data were used as features in various machine learning models. We evaluated XGBoost, random forest, MLP, and LSTM models. Our results show that LSTM outperforms the other models, and that complex models are better at capturing sentiment and trading patterns, which play a key role in accurate cryptocurrency price prediction.

\textbf{keywords:}Sentiment Analysis, Bitcoin Price Prediction,Random Forest,XGBoost,MLP, LSTM

\end{abstract}


\section{INTRODUCTION}

Bitcoin, the first decentralized cryptocurrency, has gained immense popularity over the past decade, attracting both individual and institutional investors. Known for its high volatility and speculative nature, Bitcoin’s price movements are influenced by a wide range of factors, including market sentiment, media coverage, and social media discussions. Unlike traditional financial assets, Bitcoin does not have underlying corporate earnings or physical assets that anchor its price. Instead, its value is largely driven by market perception and investor sentiment, which makes forecasting Bitcoin prices particularly challenging.

In recent years, sentiment analysis has emerged as a powerful tool in financial forecasting. By analyzing text data from various sources, sentiment analysis aims to quantify the emotions and opinions expressed by individuals, providing valuable insights into market trends. In the case of Bitcoin, social media platforms like Reddit have become vital sources of sentiment data. Reddit, with its large and active community of users discussing Bitcoin and related topics, offers a wealth of real-time sentiment that can potentially influence Bitcoin’s price movements. Studies have shown that sentiment from social media platforms like Twitter and Reddit can significantly impact financial markets, including Bitcoin, by affecting investor behavior.

While sentiment analysis has been successfully applied to stock market prediction, its application to cryptocurrency markets, particularly Bitcoin, presents unique challenges. Cryptocurrency markets are highly speculative and are driven by factors that are not present in traditional markets. Furthermore, the decentralized nature of cryptocurrencies means that their price fluctuations are often influenced by external factors such as regulatory news, technological developments, and, crucially, social sentiment. Thus, capturing the sentiment of online communities could be a valuable tool for predicting Bitcoin’s price movements.

This study aims to explore the relationship between sentiment analysis of Reddit comments and Bitcoin price prediction. By leveraging historical Bitcoin price and trading volume data, along with sentiment scores extracted from Reddit posts, we aim to evaluate the effectiveness of several machine learning models for forecasting Bitcoin’s future price. The models used in this study include traditional machine learning approaches such as XGBoost, Random Forest, and MLP, as well as deep learning techniques like Long Short-Term Memory (LSTM) networks, which are particularly suited for time-series data.

The primary contributions of this paper are as follows:
\begin{itemize}
    \item Investigating the role of sentiment analysis, specifically from Reddit comments, in predicting Bitcoin prices.
    \item Comparing the performance of different machine learning models, including traditional models (e.g., XGBoost, Random Forest) and deep learning models (e.g., LSTM), in forecasting Bitcoin prices.
    \item Providing insights into how sentiment analysis can improve the accuracy of cryptocurrency price prediction models.
\end{itemize}