Text analytics is the process of transforming unstructured text documents into usable, structured data. Text analysis works by breaking apart sentences and phrases into their components, and then evaluating each part’s role and meaning using complex software rules and machine learning algorithms.
Text analytics forms the foundation of numerous natural language processing (NLP) features, including named entity recognition, categorization, and sentiment analysis. In broad terms, these NLP features aim to answer four questions:
- Who is talking?
- What are they talking about?
- What are they saying about those subjects?
- How do they feel?
Data analysts and other professionals use text mining tools to derive useful information and context-rich insights from large volumes of raw text, such as social media comments, online reviews, and news articles. In this way, text analytics software forms the backbone of business intelligence programs, including voice of customer/customer experience management, social listening and media monitoring, and voice of employee/workforce analytics.