Recent research at Stanford (see Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, Richard Socher, et al) seems to have advanced sentiment analysis of text to a new level of about 85.7% accuracy for full sentences, if the authors are to be believed. Imagine that the state of the art might soon be approaching 100% accuracy: what would be the impact on business?
Marketing has moved dramatically in recent years away from surveys and focus groups, toward social listening: monitoring what people in social networks are saying about product, brands, and activities related to them. Marketing staff are vacuuming up comments about products, campaigns, and promotions, and trying to respond in real-time, as well as logging all the data for later analysis.
If machinery can do all or part of this, then an increasing part of the jobs of marketing, customer support, and other functions (like HR) might be accomplished by machinery geared to sentiment analysis, and AI-based approaches to taking next steps based on individual and aggregated sentiment.
It may also be that a large part of what middle managers do involves sentiment analysis. Consider tools like 15five or Tiny Pulse (see TINYpulse is a small and simple anonymous feedback tool), which are intended to be a quick technique to get a handle on staff sentiment. But if semantic analysis and AI can compbine to do all or part of that job, managers might be able to spend their time doing other things, or managing a larger number of people.
This is perhaps a codicil to the piece I wrote earlier today, How many of today’s jobs could be computerized? A whole lot. This may be part of the wave of technological advancement that the researchers I reported on in that piece were talking about.