Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers generally to the process of deriving high-quality information from text. High-quality information is typically derived through the dividing of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).
Labour-intensive manual text-mining approaches first surfaced in the mid-1980s, but technological advances have enabled the field to advance swiftly during the past decade. Text mining is an interdisciplinary field which draws on information retrieval, data mining, machine learning, statistics, and computational linguistics. As most information (over 80%) is currently stored as text, text mining is believed to have a high commercial potential value. Increasing interest is being paid to multilingual data mining: the ability to gain information across languages and cluster similar items from different linguistic sources according to their meaning.
Sentiment analysis may, for example, involve analysis of movie reviews for estimating how favorably a review is for a movie. Such an analysis may require a labeled data set or labeling of the affectivity of words. A resource for affectivity of words has been made for WordNet.
Recently, text mining has received attention in many areas.
One of the largest text mining applications that exists is probably the classified ECHELON surveillance system. Additionally, many text mining software packages such as AeroText, Attensity, SPSS and Expert System are marketed towards security applications, particularly analysis of plain text sources such as Internet news.
In 2007, Europol's Serious Crime division developed an analysis system in order to track transnational organized crime. This Overall Analysis System for Intelligence Support (OASIS) integrates among the most advanced text analytics and text mining technologies available on today's market. This system led Europol to make the most significant progress to support law enforcement objectives at the international level. 
Main article: Biomedical text mining
A range of text mining applications in the biomedical literature has been described. One example is PubGene that combines biomedical text mining with network visualization as an Internet service. Another example, which uses ontologies with textmining is GoPubMed.org.
Software and applications
Research and development departments of major companies, including IBM and Microsoft, are researching text mining techniques and developing programs to further automate the mining and analysis processes. Text mining software is also being researched by different companies working in the area of search and indexing in general as a way to improve their results.
Online Media applications
Text mining is being used by large media companies to disambiguate information and to provide readers with greater search experiences, which in turn increases site "stickiness" and revenue. Additionally, on the back end, editors are benefiting by being able to share, associate and package news across properties, significantly increasing opportunities to monetize content.
Text mining is starting to be used in marketing as well, more specifically in analytical Customer relationship management. Coussement and Van den Poel (2008) apply it to improve predictive analytics models for customer churn (Customer attrition).
The issue of text mining is of importance to publishers who hold large databases of information requiring indexing for retrieval. This is particularly true in scientific disciplines, in which highly specific information is often contained within written text. Therefore, initiatives have been taken such as Nature's proposal for an Open Text Mining Interface (OTMI) and NIH's common Journal Publishing Document Type Definition (DTD) that would provide semantic cues to machines to answer specific queries contained within text without removing publisher barriers to public access.
Academic institutions have also become involved in the text mining initiative:
The National Centre for Text Mining, a collaborative effort between the Universities of Manchester and Liverpool, provides customised tools, research facilities and offers advice to the academic community. They are funded by the Joint Information Systems Committee (JISC) and two of the UK Research Councils. With an initial focus on text mining in the biological and biomedical sciences, research has since expanded into the areas of Social Science.
In the United States, the School of Information at University of California, Berkeley is developing a program called BioText to assist bioscience researchers in text mining and analysis.
Notable Software and applications
Research and development departments of major companies, including IBM and Microsoft, are researching text mining techniques and developing programs to further automate the mining and analysis processes. Text mining software is also being researched by different companies working in the area of search and indexing in general as a way to improve their results. There is a large number of companies that provide commercial computer programs:
• AeroText - provides a suite of text mining applications for content analysis. Content used can be in multiple languages.
• Autonomy - suite of text mining, clustering and categorization solutions for a variety of industries.
• Endeca Technologies - provides software to analyze and cluster unstructured text.
• Expert System S.p.A. - suite of semantic technologies and products for developers and knowledge managers.
• Fair Isaac - leading provider of decision management solutions powered by advanced analytics (includes text analytics).
• Inxight - provider of text analytics, search, and unstructured visualization technologies. (Inxight was bought by Business Objects that was bought by SAP AG in 2008)
• Nstein - text mining solution that creates rich metadata to allow publishers to increase page views, increase site stickiness, optimize SEO, automate tagging, improve search experience, increase editorial productivity, decrease operational publishing costs, increase online revenues
• Pervasive Data Integrator - includes Extract Schema Designer that allows the user to point and click identify structure patterns in reports, html, emails, etc. for extraction into any database
• RapidMiner/YALE - open-source data and text mining software for scientific and commercial use.
• SAS - solutions including SAS Text Miner and Teragram - commercial text analytics, natural language processing, and taxonomy softwares leveraged for Information Management.
• SPSS - provider of SPSS Text Analysis for Surveys, Text Mining for Clementine, LexiQuest Mine and LexiQuest Categorize, commercial text analytics software that can be used in conjunction with SPSS Predictive Analytics Solutions.
• Thomson Data Analyzer - Enables complex analysis on patent information, scientific publications and news.
• LexisNexis - LexisNexis is a provider of business intelligence solutions based on an extensive news and company information content set. Through the recent acquisition of Datops LexisNexis is leveraging its search and retrieval expertise to become a player in the text and data mining field.
• LanguageWare - Text Analysis libraries and customization tooling from IBM
Notable Open-source software and applications
• GATE - natural language processing and language engineering tool.
• YALE/RapidMiner with its Word Vector Tool plugin - data and text mining software.
• UIMA - UIMA (Unstructured Information Management Architecture) is a component framework for analysing unstructured content such as text, audio and video, originally developed by IBM.
Until recently websites most often used text-based lexical searches; in other words, users could find documents only by the words that happened to occur in the documents. Text mining may allow searches to be directly answered by the semantic web; users may be able to search for content based on its meaning and context, rather than just by a specific word.
Additionally, text mining software can be used to build large dossiers of information about specific people and events. For example, by using software that extracts specifics facts about businesses and individuals from news reports, large datasets can be built to facilitate social networks analysis or counter-intelligence. In effect, the text mining software may act in a capacity similar to an intelligence analyst or research librarian, albeit with a more limited scope of analysis.
Text mining is also used in some email spam filters as a way of determining the characteristics of messages that are likely to be advertisements or other unwanted material.
1. ^ Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan (2002). "Thumbs up? Sentiment Classification using Machine Learning Techniques". Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP): 79–86.
2. ^ Alessandro Valitutti, Carlo Strapparava, Oliviero Stock (2005). "Developing Affective Lexical Resources". PsychNology Journal (1): 61–83. http://www.psychnology.org/File/PSYCHNOLOGY_JOURNAL_2_1_VALITUTTI.pdf.
3. ^ ""IALEIA-LEIU Annual Conference in Boston on April 9, 2008"".
4. ^ K. Bretonnel Cohen & Lawrence Hunter (January 2008). "Getting Started in Text Mining". PLoS Computational Biology 4 (1): e20. doi:10.1371/journal.pcbi.0040020. http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0040020.
5. ^ Tor-Kristian Jenssen, Astrid Lægreid, Jan Komorowski1 & Eivind Hovig (2001). "A literature network of human genes for high-throughput analysis of gene expression". Nature Genetics 28: 21–28. doi:10.1038/ng0501-21 (inactive 20 June 2008). PMID 11326270. http://www.nature.com/ng/journal/v28/n1/abs/ng0501_21.html.
o Summary: Daniel R. Masys (2001). "Linking microarray data to the literature". Nature Genetics 28: 9–10. doi:10.1038/ng0501-9 (inactive 20 June 2008). PMID 11326264.
6. ^ Andreas Doms, Michael Schroeder (2005). "GoPubMed: exploring PubMed with the Gene Ontology". Nucleic Acids Research 33: W783–W786. doi:10.1093/nar/gki470. PMID 15980585. http://www.nature.com/ng/journal/v28/n1/abs/ng0501_21.html.
7. ^ Kristof Coussement, and Dirk Van den Poel (forthcoming 2008). "Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction". Information and Management. http://www.textmining.ugent.be.
• Ronen Feldman and James Sanger, The Text Mining Handbook, Cambridge University Press, ISBN 9780521836579
• Kao Anne, Poteet, Steve R. (Editors), Natural Language Processing and Text Mining, Springer, ISBN-10: 184628175X
• Konchady Manu "Text Mining Application Programming (Programming Series)" by Manu Konchady, Charles River Media, ISBN 1584504609
• M. Ikonomakis, S. Kotsiantis, V. Tampakas, Text Classification Using Machine Learning Techniques, WSEAS Transactions on Computers, Issue 8, Volume 4, August 2005, pp. 966-974 (http://www.math.upatras.gr/~esdlab/en/members/kotsiantis/Text%20Classification%20final%20journal.pdf)
• Approximate nonnegative matrix factorization, an algorithm used for text mining
• BioCreative text mining evaluation in biomedical literature
• Business intelligence
• Computational linguistics
• Concept Mining
• Data mining
• Information retrieval
• Name resolution
• Natural language processing
• Stop words
• Text analytics
• Text classification sometimes is considered a (sub)task of text mining.
• UIMA Unstructured Information Management Architecture from IBM.
• Web mining, a task that may involve text mining (e.g. first find appropriate web pages by classifying crawled web pages, then extract the desired information from the text content of these pages considered relevant).
• Marty Hearst: What Is Text Mining? (October, 2003)
• http://projects.ldc.upenn.edu/ace/ ACE (LDC)
• http://www.itl.nist.gov/iad/894.01/tests/ace/ ACE (NIST)
• http://www.arts-humanities.net/text_mining (Discussion group text mining)