Text miningFrom Wikipedia, the free encyclopedia Text mining, also 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 devising 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). 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 application of natural language processing (NLP) and analytical methods. 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. Contents Text mining and text analytics The term text analytics describes a set of linguistic, 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"[2] in 2004 to describe "text analytics."[3] 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,[4] 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 percent of business-relevant information originates in unstructured form, primarily text.[5] These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing. History In this paper. a vehicle manufacturer. as described by Prof.P. data mining. Disambiguation — the use of contextual clues — may be required to decide where. stock ticker symbols. and computational linguistics. Recognition of Pattern Identified Entities: Features such as telephone numbers. characterized by a word pattern. such as part of speech tagging. or content management system. for analysis. on the Web or held in a file system. text mining is believed to have a high commercial potential value. the emphasis was on numerical data stored in relational databases.Labor-intensive manual text mining approaches first surfaced in the mid-1980s. and as BI emerged in the '80s and '90s as a software category and field of practice.utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the 'action points' in an organization. rather. Text mining is an interdisciplinary field that draws on information retrieval. Text analysis processes Subtasks — components of a larger text-analytics effort — typically include: Information retrieval or identification of a corpus is a preparatory step: collecting or identifying a set of textual materials. and other types of linguistic analysis. The challenge of exploiting the large proportion of enterprise information that originates in "unstructured" form has been recognized for decades. a movie star. I suggest that to make progress we do not need fully artificial intelligent text analysis. certain abbreviations. quantities . a river crossing. This is not surprising: text in "unstructured" documents is hard to process. organizations. statistics.[citation needed ] Named entity recognition is the use of gazetteers or statistical techniques to identify named text features: people. Both incoming and internally generated documents are automatically abstracted. syntactic parsing. 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. Marti A.[6] but technological advances have enabled the field to advance during the past decade. e-mail addresses. president. or some other entity. and sent automatically to appropriate action points. many others apply more extensive natural language processing.[7] It is recognized in the earliest definition of business intelligence (BI). Luhn. for instance. database. machine learning. Hearst in the paper Untangling Text Data Mining:[8] For almost a decade the computational linguistics community has viewed large text collections as a resource to be tapped in order to produce better text analysis algorithms. I have attempted to suggest a new emphasis: the use of large online text collections to discover new facts and trends about the world itself. The emergence of text analytics in its current form stems from a refocusing of research in the late 1990s from algorithm development to application. which describes a system that will: ". and so on. "Ford" can refer to a former U." Yet as management information systems developed starting in the 1960s.. a mixture of computationally-driven and user-guided analysis may open the door to exciting new results.. As most information (common estimates say over 80%)[5] is currently stored as text. place names.S. Hearst's 1999 statement of need fairly well describes the state of text analytics technology and practice a decade later. in an October 1958 IBM Journal article by H. Although some text analytics systems apply exclusively advanced statistical methods. A Business Intelligence System. Text analytics techniques are helpful in analyzing sentiment at the entity. concept.[12] One online text mining application in the biomedical literature is PubGene that combines biomedical text mining with network visualization as an Internet service. opinion.(with units) can be discerned via regular expression or other pattern matches.[11] It is also involved in the study of text encryption/decryption. blogs. mood. and business needs. or topic level and in distinguishing opinion holder and opinion object.it runs on PubMed/PMC and can be configured. Listening Platforms Natural Language/Semantic Toolkit or Service Publishing Automated ad placement Search/Information Access Social media monitoring Security applications Many text mining software packages are marketed for security applications. and emotion.[9] Quantitative text analysis is 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. a casual personal text for the purpose of psychological profiling etc. Records Management National Security/Intelligence Scientific discovery. etc. Biomedical applications Main article: Biomedical text mining A range of text mining applications in the biomedical literature has been described. on request. Competitive Intelligence E-Discovery. especially monitoring and analysis of online plain text sources such as Internet news. fact. research.[13][14] TPX is a concept-assisted search and navigation tool for biomedical literature analyses[15] . Using this approach to classifying solutions. Coreference: identification of noun phrases and other terms that refer to the same object. especially Life Sciences Sentiment Analysis Tools. application categories include: Enterprise Business Intelligence/Data Mining. . Relationship. usually. Applications can be sorted into a number of categories by analysis type or by business function.[10] Applications The technology is now broadly applied for a wide variety of government. for national security purposes. and event Extraction: identification of associations among entities and other information in text Sentiment analysis involves discerning subjective (as opposed to factual) material and extracting various forms of attitudinal information: sentiment. to run on local literature repositories too. Additionally. to clarify information and to provide readers with greater search experiences. which in turn increases site "stickiness" and revenue. including IBM and Microsoft.[19] Such an analysis may need a labeled data set or labeling of the affectivity of words. This is especially true in scientific disciplines. Coussement and Van den Poel (2008)[17][18] apply it to improve predictive analytics models for customer churn (customer attrition). Academic institutions have also become involved in the text mining initiative: The National Centre for Text Mining (NaCTeM). such as the Tribune Company. Academic applications The issue of text mining is of importance to publishers who hold large databases of information needing indexing for retrieval. editors are benefiting by being able to share.[21] respectively. in which highly specific information is often contained within written text. Resources for affectivity of words and concepts have been made for WordNet[20] and ConceptNet. on the back end. Software applications Text mining methods and software is also being researched and developed by major firms.[22] Text based approaches to affective computing have been used on multiple corpora such as students evaluations. significantly increasing opportunities to monetize content. Within public sector much effort has been concentrated on creating software for tracking and monitoring terrorist activities. is the first publicly funded text mining centre in the world. children stories and news stories. .[16] Online media applications Text mining is being used by large media companies. Text has been used to detect emotions in the related area of affective computing. 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. associate and package news across properties. more specifically in analytical customer relationship management. to further automate the mining and analysis processes. Therefore. and by different firms working in the area of search and indexing in general as a way to improve their results.[17] Sentiment analysis Sentiment analysis may involve analysis of movie reviews for estimating how favorable a review is for a movie. Marketing applications Text mining is starting to be used in marketing as well.GoPubMed is a knowledge-based search engine for biomedical texts. research facilities and offers advice to the academic community. Software Text mining computer programs are available from many commercial and open source companies and sources. The initiative's research apps were developed to support news analytics. which only found documents containing specific userdefined words or phrases. In the United States. w-shingling Sequence mining: String and Sequence Mining . Additionally. Implications Until recently. research has since expanded into the areas of social sciences.NaCTeM is operated by the University of Manchester[23] in close collaboration with the Tsujii Lab. They are funded by the Joint Information Systems Committee (JISC) and two of the UK Research Councils (EPSRC & BBSRC). In effect. text mining software can be used to build large dossiers of information about specific people and events. the School of Information at University of California. websites most often used text-based searches. For example. through use of a semantic web. text mining can find content based on meaning and context (rather than just by a specific word). an algorithm used for text mining BioCreative text mining evaluation in biomedical literature Concept Mining Name resolution Stop words Text classification sometimes is considered a (sub)task of text mining. large datasets based on data extracted from news reports can be built to facilitate social networks analysis or counter-intelligence. first find appropriate web pages by classifying crawled web pages. but are equally useful for regular text analysis applications.[25] NaCTeM provides customised tools.net provides researchers with a free scalable solution for keyword-based text analysis.g. See also Approximate nonnegative matrix factorization. a task that may involve text mining (e. Berkeley is developing a program called BioText to assist biology researchers in text mining and analysis.[24] University of Tokyo. See List of text mining software. then extract the desired information from the text content of these pages considered relevant). private initiatives also offer tools for academic text mining: Newsanalytics. Now. With an initial focus on text mining in the biological and biomedical sciences. albeit with a more limited scope of analysis. Further. Web mining. the text mining software may act in a capacity similar to an intelligence analyst or research librarian. 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. 127–32.org/10.nih.nlm. doi:10.991833). doi:10.org/10. 15.zohosites.ncbi. ISBN 1-59147-318-7. Rajgopal.clarabridge.cmu.nih. PMID 11326270 (//www.nlm.nlm.1371%2Fjournal.html#T2) 4.. Eivind (2001).1371/journal.com/default. Thomas. ^ KDD-2000 Workshop on Text Mining (http://www.cs. 12.edu/~dunja/CFPWshKDD2000. 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(http://people.ischool. a non-profit organization. (Editor) (2007). Text Mining and its Applications to Intelligence.edu/~hearst/text-mining. ISBN 978-1-4200-5940-3 Zanasi.upenn..html) (October.org/w/index. WIT Press.itl. Wikipedia® is a registered trademark of the Wikimedia Foundation. you agree to the Terms of Use and Privacy Policy.berkeley. Linguistic Data Consortium (http://projects. 2003) Automatic Content Extraction.edu/ace/) Automatic Content Extraction. . CRM and Knowledge Management .01/tests/ace/) Retrieved from "http://en.wikipedia.ldc.nist. additional terms may apply. Text is available under the Creative Commons Attribution-ShareAlike License. Inc.