Stocksnips reads millions of articles using natural language processing and extracts relevant financial news snippets. These are attributed to the right company and then scored by Machine Learning models that have been trained to deliver accuracy.
Automated (near) real-time news analysis further expands the universe of quantified data available to security analysts and enables a richer set of analysis for rapid and actionable changes to portfolios or trading strategies.
Data descriptions
|
ticker |
Ticker symbol of security |
|
date |
Datestamp |
|
total_count |
Total count of snippets (positive + negative) |
|
positive_count |
Number of positive snippets |
|
negative_count |
Number of negative snippets |
|
stocksnips_sentiment_signal |
Proprietary sentiment signal, output is a percentage from 0-100 |
Benzinga’s data samples are intended to provide a data sample large enough for testing data quality and application for the financial markets. These sample files demonstrate a sample of the formats and content that can be delivered
