Text data mining definition
Text data mining, also known as text mining, is a process of analyzing large amounts of unstructured textual data to extract meaningful insights and patterns. It uses natural language processing (NLP), machine learning, and statistical analysis to identify and extract useful information from text. Text data mining provides valuable insights that can be used to improve decision-making, product development, customer experience, and drive business success.
Text data mining use cases
- Sentiment analysis is used to determine the opinion of customers or users towards a product, service, or brand based on their reviews, feedback, and social media posts.
- Topic modeling is used to automatically identify the topics or themes present in a large collection of documents or texts, like news articles, research papers, or customer feedback.
- Text classification is used to automatically categorize text data into predefined categories based on its content, such as spam or non-spam emails, or positive or negative reviews.
- Named Entity Recognition is used to automatically identify and extract named entities (people, organizations, and locations) from a text corpus.
- Text summarization is used to automatically generate a summary of a large amount of textual data, like news articles or research papers, to provide a quick overview of the content.
