Text analytics is the process of extracting meaningful insights, patterns, and structure from unstructured textual data. It involves techniques such as natural language processing (NLP), sentiment analysis, entity recognition, topic modeling, and keyword extraction to turn written language into data that can be analyzed and visualized. The goal is to enable data-driven decision-making using text sources like emails, customer reviews, call center transcripts, social media posts, and documents.
For data professionals, text analytics is essential for unlocking value from the vast volumes of text data that are often ignored in traditional structured data analysis. It supports use cases such as customer experience optimization, compliance monitoring, product feedback analysis, and market intelligence. Tools for text analytics range from rule-based systems to advanced machine learning models that can handle nuance, context, and ambiguity in language.