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\begin{abstract}
In this investigation, we examine the correlation between sentiment analysis of Reddit commentary and the prognostication of Bitcoin prices. Reddit, with its multifaceted subreddits dedicated to Bitcoin discourse, offers a prolific repository of sentiment data. We amassed posts and comments utilizing specific keywords and amalgamated historical Bitcoin price, trading volume, and sentiment information from June to December 2024 to project Bitcoin’s forthcoming price trajectory. Sentiment analysis was executed employing the VADER tool, and these data served as features in a variety of machine learning models. We assessed XGBoost, random forest, MLP, and LSTM architectures. Our findings indicate that LSTM surpasses the other models, and that sophisticated models are more adept at encapsulating sentiment and trading patterns, which are pivotal in precise cryptocurrency price forecasting.
\textbf{keywords:}Sentiment Analysis, Bitcoin Price Prediction, Random Forest, XGBoost, MLP, LSTM
\end{abstract}
\section{INTRODUCTION}
Bitcoin, the inaugural decentralized cryptocurrency, has amassed substantial acclaim over the past decade, attracting both individual and institutional investors. Renowned for its pronounced volatility and speculative characteristics, Bitcoin’s price fluctuations are swayed by a myriad of factors, encompassing market sentiment, media coverage, and social media deliberations. Dissimilar to traditional financial assets, Bitcoin lacks fundamental corporate earnings or tangible assets that underpin its value. Instead, its pricing is predominantly driven by market perceptions and investor sentiment, rendering the forecasting of Bitcoin prices particularly formidable.
In recent times, sentiment analysis has emerged as a potent instrument in financial forecasting. By scrutinizing textual data from diverse sources, sentiment analysis endeavors to quantify the emotions and opinions articulated by individuals, yielding invaluable insights into market trends. Concerning Bitcoin, social media platforms such as Reddit have evolved into crucial sources of sentiment data. Reddit, with its extensive and vigorous user community discussing Bitcoin and affiliated subjects, provides a wealth of real-time sentiment that could potentially sway Bitcoin’s price dynamics. Research has demonstrated that sentiment from social media channels like Twitter and Reddit can substantially affect financial markets, including Bitcoin, by influencing investor conduct.
While sentiment analysis has been adeptly employed in stock market predictions, its utilization in cryptocurrency markets, particularly Bitcoin, poses distinct challenges. Cryptocurrency markets are remarkably speculative and are propelled by factors absent in traditional markets. Furthermore, the decentralized essence of cryptocurrencies signifies that their price oscillations are frequently shaped by external influences such as regulatory developments, technological advancements, and, crucially, social sentiment. Consequently, capturing the sentiment of online communities could serve as a valuable mechanism for anticipating Bitcoin’s price trends.
This study aspires to investigate the relationship between sentiment analysis of Reddit commentary and Bitcoin price prognostication. By harnessing historical Bitcoin price and trading volume data, along with sentiment scores derived from Reddit posts, we endeavor to assess the efficacy of multiple machine learning models in forecasting Bitcoin’s prospective price. The models explored in this inquiry encompass traditional machine learning methodologies such as XGBoost, Random Forest, and MLP, in addition to 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 articulated as follows: \begin{itemize} \item Elucidating the role of sentiment analysis, specifically from Reddit commentary, in predicting Bitcoin prices. \item Comparing the efficacy of various machine learning models, including traditional models (e.g., XGBoost, Random Forest) and deep learning models (e.g., LSTM), in forecasting Bitcoin prices. \item Offering insights into how sentiment analysis can enhance the precision of cryptocurrency price prediction models. \end{itemize}