What changed was pretrained word embeddings like fasttext and GloVe. These embeddings drastically reduce the complexity of text classification problems, and re

What changed was pretrained word embeddings like fasttext and GloVe. These embeddings drastically reduce the complexity of text classification problems, and reduce the amount of data you need to produce a high quality text classifier a great deal, and in that 2 year span, understanding of how to leverage them spread far enough to reach applications like this. The world has been sleeping on leveraging NLP, given the incredible amount corporations spend paying people to read and summarize text (answering questions like "How many people are having a bad search experience?", etc). The recent leaps in NLP make for many opportunities, and many products are coming to market that allow even non-technical folks to train and utilize text classifiers. If you're thinking about getting into NLP, now is definitely the time to do it. Source: founded taggit.io