Machine learning (ML) for natural language processing (NLP) and text analytics involves using machine learning algorithms and “narrow” artificial intelligence (AI) to understand the meaning of text documents. These documents can be just about anything that contains text: social media comments, online reviews, survey responses, even financial, medical, legal and regulatory documents. In essence, the role of machine learning and AI in natural language processing and text analytics is to improve, accelerate and automate the underlying text analytics functions and NLP features that turn this unstructured text into useable data and insights.
Sentiment Analysis is the most common text classification tool that analyses an incoming message and tells whether the underlying sentiment is positive, negative our neutral.
Word2vec works great for processing bodies of text. It represents words as vectors of numbers, and the distance between two word vectors determines how similar the words are..