S&P Global: Textual Data Analytics: Sentiment Scores and Behavioral Metrics

Sentiment scores and behavioral metrics leveraging natural language processing from earnings call transcripts.


Take transcripts data one step further and look at earnings call transcripts in a quantitative fashion.

  • Implement 39 sentiment and behavioral-based metrics derived from Natural Language Processing (NLP) as well as our professional, ownership and estimate datasets into your analysis or to use as a benchmark.
  • Quickly assess the sentiment and transparency expressed on earnings calls of over 11,600 active companies.
  • Leverage provided metrics to analyze and hone in on high-impact individual sections, speaker types, and individual components of calls or as building blocks to uncover additional signals.
  • Receive updates intraday to stay current on the most recent activity.
Dataset Overview:
  • Primary Entity Type: Documents
  • Geographic Coverage: Global
  • Industry Coverage: Consumer, Energy and Utilities, Financials, Healthcare, Industrials, Materials, Real Estate, Technology, Media & Telecommunications
  • History Initiated: 2004
  • Point In Time: Yes
  • Point In Time Details: Each call transcript is updated multiple times by S&P. The scores calculated for each version are retained.
  • Data Source: Calls transcribed by S&P Global transcripts team.
About the Provider:

Your source for premium financial and alternative datasets. With the goal to source, structure, link, and deliver best-in-class data, you’re armed with differentiated and clean data to uncover insights that drive your business forward and make decisions with conviction.

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