Many AI categories apply to the business side of publishing, such as ad sales, circulation, and customer support. For this article, though, let’s look at AI applications related to the reader experience.
The simplest form of extraction is to scan for specific words or phrases. Such key word analysis doesn’t require much in the way of artificial intelligence, but it’s also very limited. One problem is that it only captures terms that users know about in advance, so it can easily miss new topics or trends entirely. It also can’t automatically associate similar terms, find relationships between terms, understand context, or measure sentiment.
The form of artificial intelligence known as natural language processing does all those things, providing much richer data to analyze. From an editor’s perspective, content analysis provides guidance about what to create next. (Outside of editorial, content analysis keeps an eye on competitor’s ad campaigns and marketing materials, providing useful market intelligence.)
While most content analysis is still based on text, AI systems are becoming better at interpreting images, video, audio, and other formats. Like text analysis, these help to understand what readers are being offered and responding to. Also like text analysis, they are far from perfect. Sentiment and emotion analysis in particular are still very limited; any system that offers them should be evaluated closely before you trust their results.