Text mining, also known as
text analytics, is the process of extract
ing useful information from unstructured or semi-structured
text data. This involves us
ing various natural language process
ing (NLP) techniques to analyze and understand the co
ntent of the
text.
Text mining can be applied to a wide range of
text data sources, includ
ing social media posts, customer reviews, news articles, and scientific papers.
The primary goal of
text mining is to uncover insights and patterns that can be used to inform decision-mak
ing and improve business outcomes. For example, a company may use
text mining to analyze customer feedback and identify common themes and issues that need to be addressed. A healthcare organization may use
text mining to analyze patient records and identify patterns in disease diagnosis and treatment.
Text mining involves several steps, includ
ing data collection, preprocess
ing, analysis, and visualization. The data is usually first cleaned and preprocessed to remove noise and irrelevant information. NLP techniques are then used to tokenize the
text, identify parts of speech, and extract entities and sentiment. The result
ing data is analyzed us
ing statistical and machine learn
ing techniques to uncover patterns and relationships.