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Text Mining: Techniques, Tools & Resources

The techniques, libraries, platforms and datasets for turning unstructured text into structured insight — with stale and moved projects noted.

82 topics•8 categories•Updated October 2026

What Is Text Mining?

3 entries

Text mining, also called text analytics, extracts structured information and patterns from unstructured text such as documents, emails, reviews, social media posts and reports. A typical pipeline collects documents, extracts text from formats such as PDF, cleans and tokenizes it, converts it into features such as word counts, TF-IDF weights or embeddings, and then applies techniques such as classification, clustering, topic modeling, named entity recognition, sentiment analysis, keyword extraction and summarization. Modern systems increasingly use pretrained transformer models and embeddings, sometimes combined with search and retrieval. Text mining applies the ideas of data mining to language data and relies on natural language processing for the linguistic steps. This page gathers libraries, platforms, datasets and learning resources.

01Data MiningCompanion page on discovering patterns in structured data. 02Text mining overview (Wikipedia)General introduction. 03Speech and Language ProcessingFree textbook by Jurafsky and Martin.

NLP Libraries & Frameworks

16 entries

The building blocks of a text mining pipeline.

04NLTKPython toolkit for teaching and research in language processing. 05NLTK repositorySource code. 06Natural Language Processing with PythonFree book that accompanies NLTK. 07spaCyIndustrial-strength NLP library. 08spaCy repositorySource code. 09GensimTopic modeling and document similarity library. Its original maintainer's company site, rare-technologies.com, now redirects elsewhere, so use the project documentation. 10Gensim repositorySource code. 11Hugging Face TransformersPretrained models for classification, extraction and generation. 12Transformers repositorySource code. 13Sentence TransformersEmbeddings for semantic search and clustering. 14Stanford CoreNLPJava toolkit for linguistic analysis. 15StanzaStanford's Python NLP library. 16FlairNLP framework for sequence labeling; the last commit was in June 2025, so check its activity. 17Apache OpenNLPMachine learning toolkit for language processing in Java. 18TextBlobSimple Python API for common text tasks. 19scikit-learn text featuresBag-of-words and TF-IDF feature extraction.

Topic Modeling, Entities, Sentiment & Keywords

19 entries

Core text mining tasks with the standard tools.

20Topic models (Wikipedia)Overview of topic modeling. 21Latent Dirichlet Allocation (Blei et al.)The original LDA paper. 22Gensim LDALDA implementation in Gensim. 23LDA and NMF in scikit-learnWorked example of topic extraction. 24MALLETJava toolkit for topic modeling and classification. 25BERTopicTopic modeling with transformer embeddings. 26BERTopic repositorySource code. 27Top2VecTopic modeling with embeddings; the last commit was in November 2024, so check its activity. 28Named-entity recognition (Wikipedia)Overview of entity extraction. 29Token classification in TransformersGuide to training and using NER models. 30Sentiment analysis (Wikipedia)Overview of sentiment analysis. 31VADERRule-based sentiment analyzer for social media text. 32Text classification in TransformersGuide to fine-tuning text classifiers. 33YAKEUnsupervised keyword extraction. 34pkeKeyphrase extraction toolkit; the last commit was in July 2023, so check its activity. 35RAKE for NLTKRapid keyword extraction; the last commit was in September 2021, so check its activity. 36Summarization in TransformersGuide to summarization models. 37NLP Models for Sentiment AnalysisModels for sentiment analysis; page on this site. 38Python Libraries for Sentiment AnalysisPython sentiment libraries; page on this site.

Document Parsing & Search

8 entries

Get text out of files and make it searchable.

39Apache TikaDetects and extracts text and metadata from many file types. 40DoclingDocument conversion for PDFs and other formats, aimed at generative AI pipelines. 41UnstructuredOpen-source tools for ingesting and preprocessing documents. 42pdfplumberExtract text and tables from PDFs. 43GROBIDExtracts structured data from scholarly PDFs; the repository now lives under grobidOrg. 44Apache LuceneSearch library behind Solr and Elasticsearch. 45Apache SolrEnterprise search platform. 46ElasticsearchSearch and analytics engine.

R & Visual Tools for Researchers

8 entries

Corpus analysis without writing much code.

47quantedaR package for quantitative text analysis. 48tm (R)Text mining framework for R. 49Text Mining with RFree book on tidy text analysis in R. 50Orange text mining add-onText mining widgets for Orange. 51KNIME text processingText processing extension for KNIME workflows. 52Voyant ToolsWeb-based reading and analysis environment for digital texts. 53AntConcFree corpus analysis toolkit. 54VOSviewerSoftware for visualizing bibliometric and text-based networks.

Commercial Text Analytics & Cloud NLP

11 entries

Vendor-supported services.

55Google Cloud Natural LanguageGoogle Cloud's text analysis API. 56Amazon ComprehendAWS service for extracting insights from text. 57Azure AI LanguageMicrosoft's language service. 58IBM Watson Natural Language UnderstandingIBM's text analysis service. 59LexalyticsText analytics vendor. 60expert.aiNatural language platform vendor. 61SAS Text Miner supportSupport page for SAS Text Miner; check SAS for the current text analytics offering. 62MAXQDAQualitative and mixed methods analysis software. 63ATLAS.tiQualitative data analysis software. 64NVivoQualitative data analysis software. 65Text Data Analytics SolutionsDirectory of text analytics vendors on this site.

Corpora, Shared Tasks & Venues

8 entries

Data and communities for text research.

66Common CrawlOpen repository of web crawl data. 67Hugging Face datasetsHub of text and other datasets. 68ACL AnthologyOpen archive of computational linguistics and NLP papers. 69ACLAssociation for Computational Linguistics. 70SemEvalSeries of evaluation workshops for semantic analysis. 71TRECText Retrieval Conference. 72BERT paperInfluential transformer-based language representation model. 73Sentence-BERT paperSentence embeddings with siamese networks.

Related Pages

9 entries

Neighbouring pages on this site.

74Natural Language ProcessingNLP hub. 75Language ModelsLanguage model directory. 76Data ScienceData science hub. 77Machine LearningMachine learning hub. 78Knowledge GraphsKnowledge graph tools and resources. 79Vector DatabasesStores for embeddings. 80RAG Architectures ElucidatedRetrieval-augmented generation. 81Speech Analytics PlatformsAnalytics on spoken language. 82Python for Data SciencePython tools for data science.

Frequently Asked Questions

What is text mining?

Extracting structured information and patterns from unstructured text, such as documents, emails, reviews and social media posts, using natural language processing and machine learning.

What is the difference between text mining and NLP?

NLP is the broader field of processing human language by computer. Text mining uses NLP and data mining methods to find useful patterns and insights in collections of text.

What are common text mining tasks?

Document classification, clustering, topic modeling, named entity recognition, sentiment analysis, keyword and keyphrase extraction, summarization and search.

Should I use topic models or embeddings?

Classic models such as LDA are fast and interpretable. Embedding-based approaches such as BERTopic can capture meaning better but depend on pretrained models. Try both on a sample and inspect the results.

How do I get text out of PDFs?

Use document parsing tools such as Apache Tika, pdfplumber, Docling or Unstructured, and check output quality, especially for tables, scans and multi-column layouts.

How current is this page?

Links and project activity were checked in October 2026. Libraries, models and vendor products change quickly, so confirm details with each project or vendor. Sites that block automated checks are kept and may need a manual click.