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Transcripts + Artificial Intelligence
Research Team Harnesses the Power of Natural Language Process (NLP) to Create New Alternative Dataset

Given the growing interest in NLP among data scientists, the S&P Global Market Intelligence Quantamental Research team created this report to demystify many aspects of NLP and provide three illustrations, with accompanying Python code, of how NLP can be used in a unique way to create a new alternative dataset. For this specific work, NLP was used to quantify the sentiment of earnings calls. In the first example, sector-level sentiment trends were generated providing insights around inflection points and accelerations. The other two illustrations are: i) stock-level sentiment changes and forward returns, and ii) language complexity of earnings calls.

  • What is NLP? – We demystify common NLP terms and provide an overview of general steps in NLP.

  • Why is NLP important? – Forty zettabytes (10^21 bytes) of data are projected to be on the internet by 2020, out of which more than eighty percent of the data are unstructured in nature, requiring NLP to process and understand.

  • How can NLP help me? – We derive insights from earnings call transcripts via NLP measuring industry-level sentiment trends or language complexity of earnings calls, and much more.

  • Where do I start? – Code for each use case is enclosed, enabling users to replicate the sentiment analysis.

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