# text mining

> process of analysing text to extract information from it

**Wikidata**: [Q676880](https://www.wikidata.org/wiki/Q676880)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Text_mining)  
**Source**: https://4ort.xyz/entity/text-mining

## Summary
Text mining is the process of analyzing text to extract meaningful information from it. It is a subfield of natural language processing (NLP) and involves techniques like keyword extraction, topic modeling, and relationship extraction to uncover patterns, trends, or insights from large volumes of unstructured text data.

## Key Facts
- Text mining is a subclass of **natural language processing (NLP)** and **text and data mining**.
- It includes specialized areas such as **biomedical text mining**, **argumentation mining**, and **topic detection and tracking**.
- Related software tools include **PolyAnalyst** (launched in 1994) and **AutoMap**, developed by CASOS at Carnegie Mellon University.
- Notable organizations in the field include **Megaputer Intelligence**, founded in 1997, which develops text mining software like **TextAnalyst**.
- Text mining is used in **science, digital humanities**, and industries like **business intelligence** and **healthcare**.
- It is also known by alternate terms like **text analytics**, **text data mining**, and **automatic text analysis**.
- The process is supported by various computational methods, including **automatic summarization** and **relationship extraction**.

## FAQs
### Q: What is the difference between text mining and text analysis?
A: While often used interchangeably, **text mining** focuses on extracting structured information from unstructured text, whereas **text analysis** is a broader term that may include qualitative interpretation. Text mining is a more automated, data-driven subset of text analysis.

### Q: What are some common applications of text mining?
A: Text mining is used for **sentiment analysis**, **topic modeling**, **information retrieval**, **biomedical research**, and **business intelligence**. It helps organizations analyze customer feedback, research papers, social media, and legal documents.

### Q: What tools are available for text mining?
A: Notable tools include **PolyAnalyst** (since 1994), **TextAnalyst**, **T-Lab**, and **AutoMap**. These software solutions support tasks like **keyword extraction**, **network text analysis**, and **predictive analytics**.

### Q: Is text mining the same as natural language processing (NLP)?
A: No, text mining is a **subfield of NLP**. While NLP encompasses a wider range of language-related tasks (e.g., machine translation, speech recognition), text mining specifically deals with extracting insights from text data.

### Q: How is text mining used in healthcare?
A: **Biomedical text mining** analyzes medical literature, patient records, and clinical trials to identify trends, drug interactions, or genetic insights. It accelerates research by processing large volumes of scientific text.

## Why It Matters
Text mining transforms vast amounts of unstructured text—such as emails, social media posts, academic papers, and news articles—into actionable knowledge. In an era of information overload, it enables businesses to derive customer insights, researchers to discover patterns in scientific literature, and governments to monitor public sentiment. By automating the extraction of key information, text mining reduces manual effort, improves decision-making, and unlocks value from data that would otherwise remain untapped. Its applications span multiple domains, from **digital humanities** to **biomedicine**, making it a critical tool for data-driven innovation.

## Notable For
- **Automation of knowledge extraction**: Converts unstructured text into structured data for analysis.
- **Cross-disciplinary applications**: Used in **science, business, healthcare, and social sciences**.
- **Specialized subfields**: Includes **biomedical text mining**, **argumentation mining**, and **topic modeling**.
- **Integration with AI**: Often combined with **machine learning** and **predictive analytics** for deeper insights.
- **Historical software tools**: **PolyAnalyst** (1994) is one of the earliest commercial text mining platforms.

## Body
### Definition and Scope
Text mining is the computational process of deriving high-quality information from text. It involves techniques such as:
- **Keyword extraction**: Identifying the most relevant terms in a document.
- **Topic modeling**: Discovering abstract topics in a collection of texts (e.g., using algorithms like LDA).
- **Relationship extraction**: Identifying connections between entities (e.g., people, organizations) mentioned in text.
- **Automatic summarization**: Condensing large documents into shorter, coherent summaries.

### Parent and Related Fields
Text mining is part of **natural language processing (NLP)**, a field at the intersection of **computer science** and **linguistics**. It is closely related to:
- **Automatic summarization**: Reducing text length while preserving key information.
- **Biomedical text mining**: Focused on medical and biological texts.
- **Argumentation mining**: Extracting logical structures and arguments from text.

### Tools and Software
Several software tools facilitate text mining:
- **PolyAnalyst** (1994): Developed by Megaputer Intelligence, it supports **data science, AI, and predictive analytics**.
- **TextAnalyst**: A text analysis tool by Megaputer.
- **T-Lab**: User-friendly software for **text analysis and corpus linguistics**.
- **AutoMap**: A **network text analysis** tool from Carnegie Mellon’s CASOS, used for extracting relational data from texts.

### Industry and Research Applications
Text mining is applied in:
- **Business intelligence**: Analyzing customer reviews, surveys, and social media for market insights.
- **Academic research**: Identifying trends in scientific literature (e.g., **topic detection and tracking**).
- **Healthcare**: Mining **biomedical texts** for drug discovery, clinical decision support, and epidemiology.
- **Digital humanities**: Studying historical texts, literary patterns, and cultural trends.

### Key Organizations and Figures
- **Megaputer Intelligence** (founded 1997, Bloomington, USA): A leader in **text mining software**.
- **Badrul Sarwar**: Machine learning engineer contributing to text mining techniques.
- **Mykola Makhortykh** and **Anne Le Calvé**: Researchers in text analysis and digital humanities.

### Technical Standards and Identifiers
Text mining is referenced in multiple knowledge bases and thesauri:
- **Wikidata ID**: Q770780
- **BabelNet ID**: 02540773n
- **UNESCO Thesaurus ID**: concept2196
- **GitHub Topic**: [text-mining](https://github.com/topics/text-mining)

## Schema Markup
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  "@type": "Thing",
  "name": "text mining",
  "description": "process of analysing text to extract information from it",
  "url": "https://en.wikipedia.org/wiki/Text_mining",
  "sameAs": [
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## References

1. [Source](https://id.ndl.go.jp/auth/ndlsh/01119322)
2. Freebase Data Dumps. 2013
3. Czech National Authority Database
4. BabelNet
5. Quora
6. National Library of Israel Names and Subjects Authority File
7. KBpedia
8. [text-mining · GitHub Topics · GitHub](https://github.com/topics/text-mining)
9. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)