We integrated our text analytics with Evercore ISI Research to analyze a decade of transcript data on historical trends in trade war sentiment from eanings call transcripts.
Given the geo-political issues around trade, Evercore ISI Research applied our natural language processing (NLP) model to analyze the prevalence of trade war sentiment with the goal of objectively defining company concerns about trade. This project involved applying our NLP model on quarterly earnings call transcripts going back to 2003.
In this white paper our text analytics model extracted commentary regarding the topic of trade war along with the sentiment (positive and negative) to understand the historical pattern around trade and its implications.
To obtain a copy of this analysis for the November 9, 2018 publication: Tracking Sentiment With Natural Language Processing, email: firstname.lastname@example.org
This communication does not represent investment advice. Transcript text provided by S&P Global Market Intelligence. Analysis provided in collaboration with Evercore ISI Research.
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