News Sentiment Dataset

Access AI-powered sentiment analysis derived from financial news and social media. FinBrain’s NLP models process thousands of articles daily to generate sentiment scores that help you gauge market mood and momentum.

What’s Included

The News Sentiment dataset provides:

Sentiment Score: Normalized numeric score from -1 (bearish) to +1 (bullish) for each date
Array-Based Data: Historical sentiment returned as an array of date/score objects
Daily Updates: Fresh sentiment scores every trading day
Historical Data: Years of sentiment history for backtesting
Flexible Filtering: Filter by date range or limit the number of results

Coverage

Market	Tickers	Update Frequency
S&P 500	500+	Daily
NASDAQ	3,000+	Daily
NYSE	2,500+	Daily
Crypto	100+	Daily

Understanding Sentiment Scores

Range	Interpretation
0.5 to 1.0	Strong bullish sentiment
0.2 to 0.5	Moderate bullish sentiment
-0.2 to 0.2	Neutral sentiment
-0.5 to -0.2	Moderate bearish sentiment
-1.0 to -0.5	Strong bearish sentiment

Sentiment scores are returned as numbers in the v2 API.

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.sentiments.ticker("AAPL", as_dataframe=True)
print(df)

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.finbrain.tech/v2"
headers = {"Authorization": f"Bearer {API_KEY}"}

# Get sentiment for AAPL
response = requests.get(f"{BASE_URL}/sentiment/AAPL", headers=headers)
result = response.json()

for entry in result["data"]:
    print(f"{entry['date']}: {entry['score']}")

You can also filter by date range or limit results:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Get sentiment with date range
df = fb.sentiments.ticker("AAPL", date_from="2025-01-01", date_to="2025-06-30",
                          as_dataframe=True)
print(df.head(30))

For complete code examples in Python, JavaScript, C++, Rust, and cURL, see the API Reference.

Visualization

Plot sentiment scores with the built-in SDK chart:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# One-line interactive sentiment chart
fb.plot.sentiments("TSLA")

Sentiments Chart — TSLA news sentiment over time

Use Cases

Sentiment-Based Trading Signals

Generate trading signals based on sentiment thresholds:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def get_sentiment_signal(symbol):
    """Generate trading signal from sentiment score"""
    df = fb.sentiments.ticker(symbol, as_dataframe=True)

    if df.empty:
        return "no_data"

    latest_score = df["score"].iloc[0]

    if latest_score > 0.5:
        return "strong_buy"
    elif latest_score > 0.2:
        return "buy"
    elif latest_score < -0.5:
        return "strong_sell"
    elif latest_score < -0.2:
        return "sell"
    else:
        return "hold"

signal = get_sentiment_signal("TSLA")
print(f"Signal: {signal}")

Sentiment Screening

Screen a watchlist for tickers with extreme sentiment:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

watchlist = ["AAPL", "GOOGL", "MSFT", "AMZN", "NVDA", "TSLA"]

bullish = []
bearish = []

for symbol in watchlist:
    df = fb.sentiments.ticker(symbol, as_dataframe=True)
    if not df.empty:
        score = df["score"].iloc[0]
        if score > 0.5:
            bullish.append((symbol, score))
        elif score < -0.5:
            bearish.append((symbol, score))

print("Bullish tickers:", bullish)
print("Bearish tickers:", bearish)

Combine with Price Predictions

Enhance prediction confidence when sentiment aligns with expected price movement:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def analyze_ticker(symbol):
    """High conviction signals when predictions and sentiment align"""
    # Get predictions
    pred_result = fb.predictions.ticker(symbol, prediction_type="daily")
    expected_short = pred_result["metadata"]["expectedShortTerm"]

    # Get sentiment
    sent_df = fb.sentiments.ticker(symbol, as_dataframe=True)
    sent_score = sent_df["score"].iloc[0]

    # Stronger signal when expected move and sentiment align
    if expected_short > 0.5 and sent_score > 0.3:
        return "high_conviction_buy"
    elif expected_short < -0.5 and sent_score < -0.3:
        return "high_conviction_sell"
    else:
        return "mixed_signals"

result = analyze_ticker("AAPL")
print(result)

Sentiments API Reference - Endpoint details, parameters, and response schema
Sentiment-Based Strategies Guide - Build trading strategies
AI Price Forecasts - Combine with predictions