# A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques

**Authors:** Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut  
**arXiv:** [1707.02919](https://arxiv.org/abs/1707.02919)  
**Date:** 2017-07-10  
**Summary page:** https://www.summarizepaper.com/en/paper/1707.02919v2/

## Key points

- The paper provides a comprehensive overview of text mining techniques and their significance in handling unstructured text data.
- The authors emphasize the need for efficient techniques and algorithms to extract meaningful information from vast amounts of text generated daily.
- Text mining involves discovering useful patterns from text and has gained significant attention in recent years.
- The paper focuses on fundamental tasks such as pre-processing, classification, and clustering for organizing and understanding large amounts of textual data.
- The authors provide insights into how text mining is applied in biomedical and healthcare domains, highlighting practical applications in these fields.
- Overall, the paper serves as a valuable resource for researchers and practitioners interested in extracting meaningful information from textual sources.

## Summary

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The paper titled "A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques" by Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, and Krys Kochut provides a comprehensive overview of text mining techniques and their significance in handling the ever-increasing volume of unstructured text data. The authors discuss the challenges posed by the vast amount of text generated daily and emphasize the need for efficient techniques and algorithms to extract meaningful information from it. Text mining has gained significant attention in recent years as a task that involves discovering useful patterns from text. The paper focuses on several fundamental tasks such as pre-processing, classification, and clustering that are essential for organizing and understanding large amounts of textual data. In addition to discussing these techniques, the authors also provide insights into how text mining is applied in biomedical and healthcare domains. This additional context highlights the practical applications of text mining in these specific fields. Overall, this paper serves as a valuable resource for researchers and practitioners interested in extracting meaningful information from textual sources. It offers a comprehensive overview of key concepts and techniques in text mining and their importance in managing unstructured text data.