Text mining, text data mining ( TDM) or text analytics is the process of deriving high-quality information from plain text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources."[Hotho, A., Nürnberger, A. and Paaß, G. (2005). "A brief survey of text mining". In Ldv Forum, Vol. 20(1), p. 19-62] 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 interest. 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).
Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via the application of natural language processing (NLP), different types of and analytical methods. An important phase of this process is the interpretation of the gathered information.
A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The document is the basic element when starting with text mining. Here, we define a document as a unit of textual data, which normally exists in many types of collections.[Feldman, R. and Sanger, J. (2007). The text mining handbook. Cambridge University Press. New York]
Text analytics
Text analytics describes a set of
linguistics,
statistical, and
machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis,
research, or investigation.
[[1] ] The term is roughly synonymous with text mining; indeed,
Ronen Feldman modified a 2000 description of "text mining"
in 2004 to describe "text analytics".
[[2] ] The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s,
notably life-sciences research and government intelligence.
The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80% of business-relevant information originates in unstructured form, primarily text.
Text analysis processes
Subtasks—components of a larger text-analytics effort—typically include:
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Dimensionality reduction is an important technique for pre-processing data. It is used to identify the root word for actual words and reduce the size of the text data.
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Information retrieval or identification of a text corpus is a preparatory step: collecting or identifying a set of textual materials, on the Web or held in a file system, database, or content corpus manager, for analysis.
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Although some text analytics systems apply exclusively advanced statistical methods, many others apply more extensive natural language processing, such as part of speech tagging, syntactic parsing, and other types of linguistic analysis.
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Named entity recognition is the use of gazetteers or statistical techniques to identify named text features: people, organizations, place names, stock ticker symbols, certain abbreviations, and so on.
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Disambiguation—the use of contextual clues—may be required to decide where, for instance, "Ford" can refer to a former U.S. president, a vehicle manufacturer, a movie star, a river crossing, or some other entity.
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Recognition of pattern-identified entities: Features such as telephone numbers, e-mail addresses, quantities (with units) can be discerned via regular expression or other Pattern matching.
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Document clustering: identification of sets of similar text documents.
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Coreference resolution: identification of and other terms that refer to the same object.
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Extraction of relationships, facts and events: identification of associations among entities and other information in texts.
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Sentiment analysis: discerning of subjective material and extracting information about attitudes: sentiment, opinion, mood, and emotion. This is done at the entity, concept, or topic level and aims to distinguish opinion holders and objects.
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Quantitative text analysis: a set of techniques stemming from the social sciences where either a human judge or a computer extracts semantic or grammatical relationships between words in order to find out the meaning or stylistic patterns of, usually, a casual personal text for the purpose of psychological profiling etc.
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Pre-processing usually involves tasks such as tokenization, filtering and stemming.
Applications
Text mining technology is now broadly applied to a wide variety of government, research, and business needs. All these groups may use text mining for records management and searching documents relevant to their daily activities. Legal professionals may use text mining for
e-discovery, for example. Governments and military groups use text mining for national security and intelligence purposes. Scientific researchers incorporate text mining approaches into efforts to organize large sets of text data (i.e., addressing the problem of unstructured data), to determine ideas communicated through text (e.g., sentiment analysis in
social media) and to support scientific discovery in fields such as the
life sciences and
bioinformatics. In business, applications are used to support competitive intelligence and automated
ad serving, among numerous other activities.
Security applications
Many text mining software packages are marketed for security applications, especially monitoring and analysis of online plain text sources such as
Internet news,
, etc. for national security purposes.
It is also involved in the study of text
encryption/
decryption.
Biomedical applications
A range of text mining applications in the biomedical literature has been described,
including computational approaches to assist with studies in
protein docking,
protein interactions,
and protein-disease associations.
In addition, with large patient textual datasets in the clinical field, datasets of demographic information in population studies and adverse event reports, text mining can facilitate clinical studies and precision medicine. Text mining algorithms can facilitate the stratification and indexing of specific clinical events in large patient textual datasets of symptoms, side effects, and comorbidities from electronic health records, event reports, and reports from specific diagnostic tests.
One online text mining application in the biomedical literature is
PubGene, a publicly accessible
search engine that combines biomedical text mining with network visualization.
is a knowledge-based search engine for biomedical texts. Text mining techniques also enable us to extract unknown knowledge from unstructured documents in the clinical domain
Software applications
Text mining methods and software is also being researched and developed by major firms, including
IBM and
Microsoft, to further automate the mining and analysis processes, and by different firms working in the area of search and indexing in general as a way to improve their results. Within the public sector, much effort has been concentrated on creating software for tracking and monitoring terrorist activities.
[[3] ] For study purposes, Weka software is one of the most popular options in the scientific world, acting as an excellent entry point for beginners. For Python programmers, there is an excellent toolkit called NLTK for more general purposes. For more advanced programmers, there's also the
Gensim library, which focuses on word embedding-based text representations.
Online media applications
Text mining is being used by large media companies, such as the
Tribune Company, to clarify 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.
Business and marketing applications
Text analytics is being used in business, particularly, in marketing, such as in customer relationship management.
Coussement and Van den Poel (2008)
apply it to improve predictive analytics models for customer churn (customer attrition).
Text mining is also being applied in stock returns prediction.
Sentiment analysis
Sentiment analysis may involve analysis of products such as movies, books, or hotel reviews for estimating how favorable a review is for the product.
Such an analysis may need a labeled data set or labeling of the affectivity of words.
Resources for affectivity of words and concepts have been made for
WordNet and
ConceptNet,
respectively.
Text has been used to detect emotions in the related area of affective computing.
Scientific literature mining and academic applications
The issue of text mining is of importance to publishers who hold large
of information needing indexing for retrieval. This is especially true in scientific disciplines, in which highly specific information is often contained within the 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 the text without removing publisher barriers to public access.
Academic institutions have also become involved in the text mining initiative:
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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
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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.
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The Text Analysis Portal for Research (TAPoR), currently housed at the University of Alberta, is a scholarly project to catalogue text analysis applications and create a gateway for researchers new to the practice.
Methods for scientific literature mining
Computational methods have been developed to assist with information retrieval from scientific literature. Published approaches include methods for searching,
determining novelty,
and clarifying
among technical reports.
Digital humanities and computational sociology
The automatic analysis of vast textual corpora has created the possibility for scholars to analyze
millions of documents in multiple languages with very limited manual intervention. Key enabling technologies have been parsing, machine translation, topic
categorization, and machine learning.
The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale, turning textual data into network data. The resulting networks, which can contain thousands of nodes, are then analyzed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.[Network analysis of narrative content in large corpora; S Sudhahar, G De Fazio, R Franzosi, N Cristianini; Natural Language Engineering, 1-32, 2013] This automates the approach introduced by quantitative narrative analysis,[Quantitative Narrative Analysis; Roberto Franzosi; Emory University © 2010] whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.
Content analysis has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents.[I. Flaounas, M. Turchi, O. Ali, N. Fyson, T. De Bie, N. Mosdell, J. Lewis, N. Cristianini, The Structure of EU Mediasphere, PLoS ONE, Vol. 5(12), pp. e14243, 2010.][Nowcasting Events from the Social Web with Statistical Learning
V Lampos, N Cristianini; ACM Transactions on Intelligent Systems and Technology (TIST) 3 (4), 72][NOAM: news outlets analysis and monitoring system; I Flaounas, O Ali, M Turchi, T Snowsill, F Nicart, T De Bie, N Cristianini Proc. of the 2011 ACM SIGMOD international conference on Management of data][Automatic discovery of patterns in media content, N Cristianini, Combinatorial Pattern Matching, 2-13, 2011] The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al.[I. Flaounas, O. Ali, T. Lansdall-Welfare, T. De Bie, N. Mosdell, J. Lewis, N. Cristianini, RESEARCH METHODS IN THE AGE OF DIGITAL JOURNALISM, Digital Journalism, Routledge, 2012] showing how different topics have different gender biases and levels of readability; the possibility to detect mood patterns in a vast population by analyzing Twitter content was demonstrated as well.[Circadian Mood Variations in Twitter Content; Fabon Dzogang, Stafford Lightman, Nello Cristianini. Brain and Neuroscience Advances, 1, 2398212817744501.][Effects of the Recession on Public Mood in the UK; T Lansdall-Welfare, V Lampos, N Cristianini; Mining Social Network Dynamics (MSND) session on Social Media Applications]
Software
Text mining computer programs are available from many commercial and
open source companies and sources.
Intellectual property law
Situation in Europe
Under European copyright and database laws, the mining of in-copyright works (such as by
web mining) without the permission of the copyright owner is illegal. In the UK in 2014, on the recommendation of the Hargreaves review, the government amended copyright law
[ Researchers given data mining right under new UK copyright laws ] to allow text mining as a limitation and exception. It was the second country in the world to do so, following Japan, which introduced a mining-specific exception in 2009. However, owing to the restriction of the Information Society Directive (2001), the UK exception only allows content mining for non-commercial purposes. UK copyright law does not allow this provision to be overridden by contractual terms and conditions.
The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licenses for Europe.
Situation in the United States
US copyright law, and in particular its
fair use provisions, means that text mining in America, as well as other fair use countries such as Israel, Taiwan and South Korea, is viewed as being legal. As text mining is transformative, meaning that it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitization project of in-copyright books was lawful, in part because of the transformative uses that the digitization project displayed—one such use being text and data mining.
Situation in Australia
There is no exception in copyright law of Australia for text or data mining within the
Copyright Act 1968. The Australian Law Reform Commission has noted that it is unlikely that the "research and study"
fair dealing exception would extend to cover such a topic either, given it would be beyond the "reasonable portion" requirement.
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
as a way of determining the characteristics of messages that are likely to be advertisements or other unwanted material. Text mining plays an important role in determining financial
market sentiment.
See also
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Concept mining
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Document processing
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Full text search
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List of text mining software
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Market sentiment
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Name resolution (semantics and text extraction)
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Named entity recognition
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News analytics
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Ontology learning
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Record linkage
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Sequential pattern mining (string and sequence mining)
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w-shingling
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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)
Citations
Sources
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Ananiadou, S. and McNaught, J. (Editors) (2006). Text Mining for Biology and Biomedicine. Artech House Books.
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Bilisoly, R. (2008). Practical Text Mining with Perl. New York: John Wiley & Sons.
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Feldman, R., and Sanger, J. (2006). The Text Mining Handbook. New York: Cambridge University Press.
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Hotho, A., Nürnberger, A. and Paaß, G. (2005). "A brief survey of text mining". In Ldv Forum, Vol. 20(1), p. 19-62
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Indurkhya, N., and Damerau, F. (2010). Handbook of Natural Language Processing, 2nd Edition. Boca Raton, FL: CRC Press.
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Kao, A., and Poteet, S. (Editors). Natural Language Processing and Text Mining. Springer.
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Konchady, M. Text Mining Application Programming (Programming Series). Charles River Media.
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Manning, C., and Schutze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press.
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Miner, G., Elder, J., Hill. T, Nisbet, R., Delen, D. and Fast, A. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Elsevier Academic Press.
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McKnight, W. (2005). "Building business intelligence: Text data mining in business intelligence". DM Review, 21–22.
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Srivastava, A., and Sahami. M. (2009). Text Mining: Classification, Clustering, and Applications. Boca Raton, FL: CRC Press.
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Zanasi, A. (Editor) (2007). Text Mining and its Applications to Intelligence, CRM and Knowledge Management. WIT Press.
External links
