With the text analytics market seen growing from 2017’s $3.9 billion to an expected $9 billion by 2025, it is no wonder that many companies are rushing to gear up into machine learning AI for text analytics.
Experts warn, however, that the lemming-like rush into machine learning for text analytics could lead some firms over the cliff. It’s not all about the shiny new object; tried-and-true methods for text analytics still do the job as good — or better — than new methods.
Alex Woodie reports on the surprising text analytics space in Datanami:
According to text analytics experts, in the rush to utilize AI, companies are missing the opportunity to use well-established techniques to harness the meanings, trends, and human emotions embodied in all those words, and in the process become a contextually driven enterprise.
Text analytics is a well-trod branch of data mining that essentially turns unstructured text into structured data, using natural language processing (NLP) and other techniques, so that it can be analyzed in an automated and scalable manner. While text is considered unstructured, there is an enormous amount of complexity and nuance contained in high-level human language, which makes text analytics extremely fertile ground for gleaning insights about people and what they’re thinking and feeling.
Companies have been mining text for decades to identify trends and uncover insights contained within large collections of words. While the technologies behind text mining have evolved a bit thanks to novel machine learning approaches, some of the most effective text mining techniques have not changed significantly in years. “Don’t fall for the hype that AI will solve all of your text analytics needs,” writes Forrester analyst Boris Evelson in his June round-up of text analytics platforms. “Just the opposite; in this evaluation we found that rules still rule. Mostly rules-based text analytics platforms are much more accurate out of the box and require much less training than platforms based mostly on machine learning.”
