• Delivery Frequency/Timezone:

    Minute, Hourly or Daily/ UTC+0

  • History:

    From 2014

  • Coverage:

    Global Equities

  • Format:


SESAMm strives to make big data easy to ingest. Their NLP engine works through +250 thousand data sources to create a sentiment based leading indicator.

Their output is focused on a robust, easy to understand format that allows filtering by financial vs non-financial text, filter by data source (news, social trading, blogs, etc) and by language. The ability to broadcast by different latencies allows SESAMm to serve all aspects of the financial industry.

Data descriptions




Are included for each time period all indicators available between beginning of day/hour/minute till the end of specified day/hour/minute; e.g. hour 02:00:00 corresponds to data gathered from 02:00:00 until 02:59:59



Number of mentions to the ticker specified for the corresponding time period


Internal unique identifier matched with a ticker



Ticker’s name as string, subject to change when companies get delisted or acquired for example





Experts users messages (represented as 1) vs. Global users messages (represented as 0). Experts users are professional traders and influencers in social trading communities





Language of all the mentions for each line of data.


cn = Chinese, de = German, en = English, es = Spanish, fr = French, it = Italian, ja = Japanese, pt = Portuguese




Type of data source for articles / messages in each line

0 = news, 1 = blogs, 2 = discussions (forums and social media), 3 = social trading


Financial data, typically from trading-oriented websites (represented as 1), or non-financial data, typically consumers

data (represented as 0)






Traders’ opinions (bullish or bearish), only available for Social Trading data

Average Trading Opinion for a specific period is found by

dividing the given value by the total Volume (thus indicating a Trading Opinion between -1 and 1)





Neutral, Positive, Negative


Average Sentiment value for a specific period is found by dividing each Sentiment by the total Volume





Neutral, Joy, Fear, Sadness, Anger, Surprise


Average Emotion value for a specific period is found by dividing each Emotion by the total Volume during that period

 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