Daily S-Score Analysis: Sentiment Insights in Cryptocurrency Markets

As equity markets continue to evolve under the influence of macroeconomic factors, geopolitical events, and shifting investor sentiment, digital assets have emerged as an increasingly attractive alternative. Institutional and retail interest in digital assets has increased, driving demand for AI-powered market analytics capable of identifying actionable trading signals in this fast-moving ecosystem.

Measuring Sentiment for Cryptocurrencies

At Context Analytics, we apply advanced Natural Language Processing (NLP) methodologies to perform large-scale sentiment analysis and quantify market sentiment across a wide range of digital assets. Our sentiment framework tracks 865 actively traded cryptocurrencies, evaluating real-time social media conversations to measure investor tone and emotion.

Sentiment data from Twitter is captured daily at 23:55 UTC, serving as a snapshot of market mood for each coin. The sentiment metric, S-Score, represents a standardized sentiment score that measures the tone of market discussions over the past 24 hours, compared against a 20-day rolling baseline. We then analyze how S-Score correlates with subsequent daily returns, allowing us to assess whether positive or negative sentiment provides predictive insight into short-term price movements.

Methodology: S-Score Tertile Analysis 

To investigate this relationship, all covered cryptocurrencies were grouped into tertiles based on their S-Scores at the 23:55 UTC timestamp:

• Tertile 1 (Bottom 33%) – Assets with the most negative sentiment scores.
• Tertile 2 (Middle 33%) – Assets exhibiting neutral or mixed sentiment.
• Tertile 3 (Top 33%) – Assets showing the highest positive sentiment levels.

We then computed the daily returns for each tertile across the dataset, spanning from January 1, 2024 to September 30, 2025.

Correlation Between Sentiment and Market Returns 

The chart above reveals a clear correlation between sentiment and subsequent cryptocurrency daily returns.

• The bottom tertile consistently underperformed, suggesting that negative sentiment is often associated with short-term price declines.
• The middle tertile produced moderate and relatively stable returns.
• The top tertile demonstrated the strongest performance, with substantially higher returns and risk-adjusted ratios

Key performance metrics, including Cumulative Return, Annualized Return, Sharpe Ratio, and Sortino Ratio, all increased progressively with higher tertile rankings. This pattern suggests that positive sentiment is frequently a precursor to stronger short-term price performance, whereas negative sentiment often signals weaker outcomes.

Integrating NLP into Crypto Trading Strategies 

This study underscores the growing importance of sentiment analytics in understanding and navigating the cryptocurrency market. As digital assets continue to mature and attract broader participation, Context Analytics’ NLP-driven sentiment metrics provide a valuable quantitative lens for traders, portfolio managers, and researchers seeking to capture market sentiment in real time. By integrating sentiment data into trading and risk management strategies, investors can gain a competitive informational edge—enhancing both decision quality and performance outcomes. For more information on Context Analytics’ sentiment data, please visit www.contextanalytics-ai.com or click the button below to learn more.

TL;DR:

Context Analytics analyzed sentiment for 865 cryptocurrencies using S-Score metrics derived from Twitter data. Cryptocurrencies with the most positive sentiment (top tertile) significantly outperformed those with negative sentiment, achieving 191.52% cumulative returns vs. 35.33% for the bottom tertile over 21 months. The results demonstrate that NLP-driven sentiment analysis provides actionable trading signals in crypto markets.