Business Applications of AI-Powered NLP

Problems With Machine Learning

  • disambiguation: words, meanings, and context are difficult to nail down through boolean structures. For example, Anne Hathaway causes Berkshire Hathaway stock to go up because of poor noise filtering.
  • sarcasm and slang: understanding embedded meanings of difficult slang is notoriously difficult with rules-based approaches. These approaches typically achieve 60% accuracy versus the human gold standard (three different people in agreement).
  • missing implicit meaning: rules-based approaches often miss implicit meaning when scraping for sentiment analysis.
  • poor recall: doing document-level analysis misses key signals when searching for opinions. For example, this tweet could miss all the implicit opinions in favor of Verizon and deprecatory of Sprint.

New Approaches

But Can AI-Powered Social Listening Data Predict The Future?

Where Does This Take Us?

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