AI-Driven Sentiment Analysis for Bitcoin Market Trends: A Predictive Approach to Crypto Volatility - Bhowmik, Proshanta Kumar; Chowdhury, Faiaz Rahat; Sumsuzzaman, Md; Ray, Rejon Kumar; Khan, Medhat Mohiuddin; Gomes, Clinton Anthony Heaven; Al Helal, Md Abdullah; Siam, Md Abubokor; Gomes, Clapher Ankur (Journal of Ecohumanism, 2025)
Cryptocurrency markets in the USA, especially that of Bitcoin, are plagued by extreme volatility fueled by a dynamic intersection of macroeconomic forces, speculator behavior, and sentiment of investors. Conventional financial models cannot keep pace with the high-frequency price changes typical of digital assets, prompting the need for novel methodologies that can better account for unstructured data like social media sentiment, news reports, and discussion forum postings. The central aim of this study was to establish a strong model that integrates sentiment analysis and machine learning methods to forecast the price movements of Bitcoin. The dataset used included multi-source sentiment data and cryptocurrency market indicators, which allow for in-depth analysis of public emotion on cryptocurrency volatility. Sentiment was sourced from Twitter (tweet text with Bitcoin hashtags and keyword mentions), Reddit (r/Bitcoin and r/Crypto Currency subreddits), and financial headlines (Bloomberg, CoinDesk, Reuters), covering the timeframe of 2019–2024 to ensure the inclusion of various market cycles. Textual data was pre-cleaned to remove noise signals (bots, spam, non-English text) and annotated for sentiment polarity (positive, negative, neutral) using both VADER (Valence Aware Dictionary for sEntiment Reasoner) and fine-tuned BERT models for contextual relevance. In analyzing how sentiment affects the volatility of the Bitcoin market, we used various modeling methods such as Logistic Regression, Random Forest Classifier, and Support Vector Machines. Support Vector Machines stands slightly ahead in terms of accuracy, implying that it might be the strongest among the three for this particular task. Logistic Regression and Random Forest both show similar levels of accuracy, which means that both of them are also strong, though less optimal compared to the Random Forest model. The use of sentiment analysis in financial markets, especially in the cryptocurrency market, provides U.S.-based investors and traders with a valuable means of risk protection. Through the use of sentiment-aware forecasts, investors can make predictions of market trends and probable price movements based on public sentiment. Crypto-fintech platforms can leverage sentiment analysis to build real-time alert systems that update users on important market movements. Through social media and news channels, the platforms can issue alerts on impending price volatility or impending trends, allowing the user to react quickly to market forces. The capability to bring in real-time social media APIs for live predictions marks a critical leap for sentiment analysis in the cryptocurrency market. Through APIs like Twitter, Reddit, and other social media platforms, investors can get instant readings on public sentiment, which in turn will allow them to make real-time and better-informed trading decisions.