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@article{177927,
        author = {Leenapackyasri S and Priya Dharshini A and Mrs.P.Sabeena Burvin and Dr.J.Hemalatha and Dr.P.Senthil Pandian},
        title = {Cryptocurrency Price Prediction Using Machine Learning Model With Sentiment},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2569-2574},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177927},
        abstract = {Cryptocurrency price prediction remains a challenging task due to extreme market volatility and the influence of public sentiment. In this study, we present a hybrid machine learning framework that can integrate real-time and historical OHLC price data with sentiment analysis from Reddit and Wikipedia. Inspired by prior research on weighted sentiment influence, our model incorporates sentiment scores derived using the DistilBERT NLP pipeline and technical indicators such as moving averages and price change ratios. Unlike traditional LSTM-based systems, our final implementation utilizes XGBoost for its effectiveness in handling structured tabular data. The datasets are continuously collected and stored in MongoDB, enabling scalable real- time updates. Feature engineering techniques include rolling averages of sentiment, sentiment-based features, and lag- based technical signals. Experimental evaluation using RMSE and MAE metrics demonstrates the efficacy of our model in forecasting the future prices of Bitcoin, showing improved accuracy over standalone approaches. The results highlight the importance of integrating sentiment analysis, real-time market signals, and machine learning for robust financial forecasting in the cryptocurrency domain.},
        keywords = {Cryptocurrency, Feature Engineering, Hybrid Model, Price Prediction, Real-time Data, Sentiment Analysis, XGBoost.},
        month = {May},
        }