- Trending Categories
- Data Structure
- Operating System
- C Programming
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
What are the techniques of Text Mining?
Text mining is also known as text analysis. It is the procedure of transforming unstructured text into structured data for simple analysis. Text mining applies natural language processing (NLP), enabling machines to know the human language and process it automatically.
Text mining is an automatic process that uses natural language processing to extract valuable insights from unstructured text. It can be transforming data into information that devices can understand, text mining automates the procedure of defining texts by sentiment, topic, and intent.
There are the following techniques of text mining which are as follows −
Information Extraction − Information Extraction is the first step of analyzing unstructured text. It is the service of automatically extracting structured data from unstructured and semi-structured device-readable documents.
Summarization − This process has the objective of precise text from a huge number of text documents. Automatic summarization is the procedure of decreasing a text document with a computer program to make a summary that retains the most important points of the initial document. Automatic data summarization is an element of machine learning and data mining.
Topic Tracking − The concept of the topic tracking structure is to support user profiles based on previous searches and guesses other documents very efficiently based on user profiles.
Text mining is an area that automatically extracts previously unknown and useful data from unstructured textual data. It has powerful connections with natural language processing. Topic tracking is one of the technologies that has been created and can be used in the text mining process.
Classification − It is the process of discovering the main theme of files by inserting metadata and analyzing the document. This methods find counts of words and from that count decides the topic of the files. In this procedure, text documents are classified into the predefined class label.
Categorization − Text categorization is the task of assigning predefined categories to free-text documents. It can support conceptual views of document set and has important software in the real world.
Clustering − Clustering can be treated the most essential unsupervised learning problem; so, as with each other issues of this type, it deals with discovering a structure in a set of unlabeled data.
Concept Linkage − Text mining uses the technique concept linkage to find the related document. This mechanism browses documents instead of searching. It offers the facility to link related documents.
Natural Language Processing − Natural language is nothing but human language and that is processed with computer language, this whole interaction is called Natural Language Processing (NLP). The main goal of NLP is to design and form such a computer system that will examine, understand and produce NLP.
- What are the techniques of data mining?
- What are the techniques of Text Indexing?
- What are the applications of Text Mining?
- What are the techniques for Mining Negative Patterns?
- What are the areas of text mining in data mining?
- What is the techniques of statistical data mining?
- What are the techniques of Text Steganography in Information Security?
- What is the need of Text Mining?
- What is the Process of Text Mining?
- What are the techniques of Steganalysis?
- What are the techniques of Dimensional Modeling?
- What are the techniques of data Encryption?
- What are the techniques of Monoalphabetic Cipher?
- What is the difference between Text Mining and Data Mining?
- What is Text Data Mining?