It's a far more complex way of analyzing how consumers feel about our products and services, using not just simple words but longer sentence fragments. Yes, AI is becoming smart enough to understand the tone of a statement, rather than simply understanding whether certain words within a group of text have a positive or negative connotation. This is incredibly impactful for companies seeking to optimize their message, improve customer engagement, or even identify top influencers in their customer base. The possibilities of sentiment analysis are incredibly far-reaching. The types of information that AI can gather from both unstructured data and affective computing in sentiment analysis are huge.
"Man and machine always get a better answer than man alone or machine alone." "The robots are coming, the robots are coming!" said my colleague and artificial intelligence expert Kimberly Nevala in a tongue-in-cheek teaser for her new ebook, "Making Sense of AI." In fact, in the context of digital transformation and customer experience, artificial intelligence (AI) already has a foot in the door. And that foot is poised to kick the door wide open. IDC predicts that by 2019, 40 percent of digital transformation initiatives will be supported by some sort of cognitive computing or AI effort.
Text analysis is about deriving high-quality structured data from unstructured text. Another name for text analytics is text mining. A good reason for using text analytics might be to extract additional data about customers from unstructured data sources to enrich customer master data, to produce new customer insight or to determine sentiment about products and services. Entity extraction,the parsing and extracting of entities from raw text, is a key part of text analytics. In many cases, entity extraction can be turned into automated entity recognition, in which text is parsed and well-understood entities are automatically selected from the text by the software.
But how do you turn that feedback into meaningful customer insights? In the past, companies used things like surveys to try to narrow down a general good/bad/neutral response to their recent marketing campaign or product. Still, there is so much more information in the form of unstructured data that could help companies better understand their customers. Whether they are using social media, blogs, forums, reviews, or online news commenting, customers are sharing their opinions in tons of different ways every single day. The only issue: many of these opinions are shared in nuanced ways that traditional AI hasn't been able to navigate.