Text mining

Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the divining 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).

History

Labor-intensive manual text mining approaches first surfaced in the mid-1980s, but technological advances have enabled the field to advance during the past decade. Text mining is an interdisciplinary field that draws on information retrieval, data mining, machine learning, statistics, and computational linguistics. As most information (common estimates say 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.

Applications

Recently, text mining has received attention in many areas.

Security applications

Many text mining software packages are marketed towards security applications, particularly analysis of plain text sources such as Internet news.

Biomedical applications

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 text mining example is GoPubMed.org. Semantic similarity has also been used by text-mining systems, namely, GOAnnotator.

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, such as the Tribune Company, 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.

Marketing applications

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).

Sentiment analysis

Sentiment analysis may involve analysis of movie reviews for estimating how favorable 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.

Academic applications

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 the National Institutes of Health'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 (NaCTeM), is the first publicly funded text mining centre in the world. NaCTeM is operated by the University of Manchester in close collaboration with the Tsujii Lab, University of Tokyo. NaCTeM 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 (EPSRC & BBSRC). 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 biology 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.

Text mining computer programs are available from a large number of commercial and open source companies"

Commercial software and applications

• AeroText - provides a suite of text mining applications for content analysis. Content used can be in multiple languages.
• Attensity - hosted, integrated and stand-alone text mining (analytics) software that uses natural language processing technology to address collective intelligence in social media and forums; the voice of the customer in surveys and emails; customer relationship management; e-services; research and e-discovery; risk and compliance; and intelligence analysis.
• Autonomy - suite of text mining, clustering and categorization solutions for a variety of industries.
• Basis Technology - provides a suite of text analysis modules to identify language, enable search in more than 20 languages, extract entities, and efficiently search for and translate entities.
• 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).
• Linguamatics - Linguamatics helps organizations to maximize the value derived from information resources through effective deployment of innovative natural language processing (NLP) based technology built into it's flagship product, I2E.
• Inxight - provider of text analytics, search, and unstructured visualization technologies. (Inxight was bought by Business Objects that was bought by SAP AG in 2008).
• LanguageWare - text analysis libraries and customization tooling from IBM.
• LexisNexis - 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.
• Nstein Technologies - 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. In combination with search engines it is used to create semantic search applications.
• SAS - solutions including SAS Text Miner and Teragram - commercial text analytics, natural language processing, and taxonomy software leveraged for Information Management.
• Silobreaker - provides text analytics, clustering, search and visualization technologies.
• 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.
• StatSoft - provides STATISTICA Text Miner as an optional extension to STATISTICA Data Miner, for Predictive Analytics Solutions.
• Thomson Data Analyzer - enables complex analysis on patent information, scientific publications and news.

Open-source software and applications

• GATE - natural language processing and language engineering tool.
• UIMA - UIMA (Unstructured Information Management Architecture) is a component framework for analysing unstructured content such as text, audio and video, originally developed by IBM.
• YALE/RapidMiner with its Word Vector Tool plugin - data and text mining software.
• Carrot2 - text and search results clustering framework.

Implications

Until recently, websites most often used text-based searches, which only found documents containing specific user-defined words or phrases. Now, through use of a semantic web, text mining can find content based on 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, large datasets based on data extracted from news reports 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.

See also

• 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
• National Centre for Text Mining (NaCTeM)
• 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).
• w-shingling

References

• Sophia Ananiadou and John McNaught (Editors), Text Mining for Biology and Biomedicine, Artech House Books (2006), ISBN 978-1-58053-984-5
• 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 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)

External links

• Marti Hearst: What Is Text Mining? (October, 2003)