Bitcoin Price Prediction with a Hybrid Wavelet Transform-LSTM Approach and Big News Sentiment Data Fusion: Evidence from Multiscale Analysis and Big Data Text Mining
Description
Title: Bitcoin Price and News Sentiment Dataset (2018–2025) Authors: Saeed Kianpoor, Shahram Fattahi, Reza Shamsolahi Abstract: This dataset comprises daily Bitcoin price data and corresponding news sentiment scores spanning the period from January 2018 to December 2025. The price data include open, high, low, close, and volume (OHLCV) for Bitcoin (BTC/USD), collected from reliable cryptocurrency market sources. The news sentiment data are derived from a large-scale collection of Bitcoin-related news articles and social media posts, processed using VADER (Valence Aware Dictionary and sEntiment Reasoner) and TextBlob sentiment analysis algorithms. Sentiment metrics include daily average sentiment scores, sentiment polarity, and news volume counts. Methodology: Price Data: Sourced from CoinGecko and Yahoo Finance (BTC/USD daily OHLCV). News Data: Collected from multiple online news sources and Twitter (X) using web scraping and APIs. Sentiment Analysis: Applied VADER and TextBlob to compute daily sentiment polarity scores (ranging from -1 to +1) and subjectivity measures. Preprocessing: Data were cleaned, normalized, and aligned by date to create a unified daily time series. Missing values were handled using linear interpolation. Data Structure: The dataset includes the following variables: date: Trading date (YYYY-MM-DD) open, high, low, close, volume: Bitcoin OHLCV data sentiment_mean: Average daily sentiment score sentiment_std: Standard deviation of sentiment scores news_count: Number of news articles per day positive_ratio, negative_ratio, neutral_ratio: Sentiment category proportions Potential Applications: This dataset is suitable for: Cryptocurrency price forecasting using machine learning and deep learning models (LSTM, GRU, Transformer, etc.) Sentiment analysis and natural language processing (NLP) research in financial domains Multiscale time-series analysis (wavelet transform, empirical mode decomposition) Behavioral finance and market efficiency studies Risk management and volatility modeling (VaR, stress testing) Keywords: Bitcoin; Cryptocurrency; Sentiment Analysis; Price Prediction; LSTM; Wavelet Transform; Financial Time Series; Big Data; Natural Language Processing License: This dataset is shared under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Users are free to use, share, and adapt the data, provided appropriate credit is given to the authors. Citation: If you use this dataset in your research, please cite it as: Kianpoor, S., Fattahi, S., & Shamsolahi, R. (2026). Bitcoin Price and News Sentiment Dataset (2018–2025). Mendeley Data. [DOI will be assigned upon publication]