Text analysis is a broad term that encompasses the examination and interpretation of textual data. It involves various techniques to understand, organize, and derive insights from text, including methods from linguistics, statistics, and machine learning. Text analysis often includes processes like text categorization, sentiment analysis, and entity recognition, to gain valuable insights from textual data.
Text mining, also known as text data mining, is a process of using computer programs and algorithms to dig through large amounts of text, like books, articles, websites, or social media posts, to find valuable and hidden information. This information could be patterns, trends, insights, or specific pieces of knowledge that are not immediately obvious when you read the texts on your own. Text data mining helps people make sense of vast amounts of text data quickly and efficiently, making it easier to discover useful information and gain new perspectives from written content.
This video is an introduction to text mining and how it can be used in research.
There are many different methods for text analysis, such as:
These text analysis techniques serve various purposes, from organizing and understanding text data to making predictions, extracting knowledge, and automating tasks.
Before beginning your text analysis project, it is important to specify your goals and then choose the method that will allow you to meet those goals. Then, consider how much data you need, and identify a sampling plan, before beginning data collection.
