Text analytics, also known as text mining or natural language processing (NLP), is the process of analyzing unstructured text data to extract useful insights and information. It involves using software tools and algorithms to automatically identify patterns and trends in large volumes of text data.
Text analytics is used to extract insights from a wide range of text-based data sources, such as social media posts, customer feedback, product reviews, news articles, and emails. It can be used to analyze text data to identify sentiment, key topics, and trends, and to understand how people are talking about a particular topic or brand.
Some common techniques used in text analytics include sentiment analysis, entity recognition, topic modeling, and text classification. These techniques can be used to analyze large volumes of text data and to identify key insights and patterns.
Overall, text analytics is a powerful tool for analyzing and understanding unstructured text data. By leveraging text analytics techniques, businesses can gain insights into customer sentiment, identify emerging trends, and make data-driven decisions to improve their operations and customer experiences.