A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
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Abstract (arXiv)
The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jul 2017.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 5; 2025: 3.
12 papers.
- 2024Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialoguescited by 11
- 2024Incubating Text Classifiers Following User Instruction with Nothing but LLMcited by 6
- 2025Hybrid OCR-LLM Framework for Enterprise-Scale Document Information Extraction Under Copy-heavy Taskcited by 5
- 2022Is Your Model Sensitive? SPeDaC: A New Benchmark for Detecting and Classifying Sensitive Personal Datacited by 3
- 2021DocSCAN: Unsupervised Text Classification via Learning from Neighborscited by 2
- 2021Large-Scale Data Mining of Rapid Residue Detection Assay Data From HTML and PDF Documents: Improving Data Access and Visualization for Veterinarianscited by 2
- 2022No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modelingcited by 2
- 2025Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)cited by 1
- 2024A Machine Learning Ensemble Model for the Detection of Cyberbullyingcited by 0
- 2024Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Trainingcited by 0
- 2024Enriching Social Science Research via Survey Item Linkingcited by 0
- 2025Multivariate Gaussian Topic Modelling: A novel approach to discover topics with greater semantic coherencecited by 0
- 2025Hybrid OCR-LLM Framework for Enterprise-Scale Document Information Extraction Under Copy-heavy Task11 Oct 2025
- 2025Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)3 Jul 2025
- 2025Multivariate Gaussian Topic Modelling: A novel approach to discover topics with greater semantic coherence19 Mar 2025
- 2024Enriching Social Science Research via Survey Item Linking20 Dec 2024
- 2024Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues12 Dec 2024
- 2024Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training22 Sep 2024
- 2024Incubating Text Classifiers Following User Instruction with Nothing but LLM16 Apr 2024
- 2024A Machine Learning Ensemble Model for the Detection of Cyberbullying19 Feb 2024
- 2022Is Your Model Sensitive? SPeDaC: A New Benchmark for Detecting and Classifying Sensitive Personal Data12 Aug 2022
- 2022No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling2 Aug 2022
- 2021Large-Scale Data Mining of Rapid Residue Detection Assay Data From HTML and PDF Documents: Improving Data Access and Visualization for Veterinarians2 Dec 2021
- 2021DocSCAN: Unsupervised Text Classification via Learning from Neighbors9 May 2021