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Encyclopedia of Machine Learning pp 397–401Cite as

Feature Construction in Text Mining

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Synonyms

Feature generation in text mining

Definition

Feature construction in text mining consists of various techniques and approaches which convert textual data into a feature-based representation. Since traditional machine learning and data mining techniques are generally not designed to deal directly with textual data, feature construction is an important preliminary step in text mining, converting source documents into a representation that a data mining algorithm can then work with. Various kinds of feature construction approaches are used in text mining depending on the task that is being addressed, the data mining algorithms used, and the nature of the dataset in question.

Motivation and Background

Text mining is the use of machine learning and data mining techniques on textual data. This data consists of natural language documents that can be more or less structured, ranging from completely unstructured plain text to documents with various kinds of tags containing...

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Recommended Reading

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Author information

Authors and Affiliations

    Authors
    1. Janez Brank
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    2. Dunja Mladenić
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    3. Marko Grobelnik
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    Editor information

    Editors and Affiliations

    1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia, 2052

      Claude Sammut

    2. Faculty of Information Technology, Clayton School of Information Technology, Monash University, P.O. Box 63, Victoria, Australia, 3800

      Geoffrey I. Webb

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    © 2011 Springer Science+Business Media, LLC

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    Brank, J., Mladenić, D., Grobelnik, M. (2011). Feature Construction in Text Mining. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_303

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