Abstract
Text data has been increasingly growing in the last years, due to the advances of web based technologies that enable the publishing of an overwhelming amount of data. One can say that, many knowledge about the world in text data, besides being stored in articles and books, is also available on blogs, tweets, web pages. This paper overviews some general techniques for text data mining, based on text retrieval models, that can be applicable to any text in natural language. The techniques are targeted to problems requiring minimum or no human effort. These techniques, which can be used in many applications, allow the discovery of main topics of a document in text data with different levels of granularity.
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This work was supported by Portuguese funds through the Center of Naval Research (CINAV), Portuguese Naval Academy, Portugal.
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Correia, A., Gonçalves, A. (2017). Topics Discovery in Text Mining. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-56535-4_25
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DOI: https://doi.org/10.1007/978-3-319-56535-4_25
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