Abstract
The extraction of useful insights from text with various types of statistical algorithms is referred to as text mining, text analytics, or machine learning from text. The choice of terminology largely depends on the base community of the practitioner. This book will use these terms interchangeably. Text analytics has become increasingly popular in recent years because of the ubiquity of text data on the Web, social networks, emails, digital libraries, and chat sites.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
C. Aggarwal. Data mining: The textbook. Springer, 2015.
C. Aggarwal, and C. Zhai, Mining text data. Springer, 2012.
R. Baeza-Yates, and B. Ribeiro-Neto. Modern information retrieval. ACM press, 2011.
R. Banchs. Text Mining with MATLAB. Springer, 2012.
C. M. Bishop. Pattern recognition and machine learning. Springer, 2007.
S. Buttcher, C. Clarke, and G. V. Cormack. Information retrieval: Implementing and evaluating search engines. The MIT Press, 2010.
S. Chakrabarti. Mining the Web: Discovering knowledge from hypertext data. Morgan Kaufmann, 2003.
W. B. Croft, D. Metzler, and T. Strohman. Search engines: Information retrieval in practice, Addison-Wesley Publishing Company, 2009.
R. Feldman and J. Sanger. The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, 2007.
J. Han, M. Kamber, and J. Pei. Data mining: concepts and techniques. Morgan Kaufmann, 2011.
T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2009.
D. Jurafsky and J. Martin. Speech and language processing. Prentice Hall, 2008.
B. Liu. Web data mining: exploring hyperlinks, contents, and usage data. Springer, New York, 2007.
C. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval. Cambridge University Press, Cambridge, 2008.
C. Manning and H. Schütze. Foundations of statistical natural language processing. MIT Press, 1999.
A. McCallum. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.cmu.edu/~mccallum/bow, 1996.
T. M. Mitchell. Machine learning. McGraw Hill International Edition, 1997.
F. Moosmann, B. Triggs, and F. Jurie. Fast Discriminative visual codebooks using randomized clustering forests. NIPS Conference, pp. 985–992, 2006.
G. Salton and M. J. McGill. Introduction to modern information retrieval. McGraw Hill, 1986.
P.-N Tan, M. Steinbach, and V. Kumar. Introduction to data mining. Addison-Wesley, 2005.
S. Weiss, N. Indurkhya, and T. Zhang. Fundamentals of predictive text mining. Springer, 2015.
C. Zhai and S. Massung. Text data management and mining: A practical introduction to information retrieval and text mining. Association of Computing Machinery/Morgan and Claypool Publishers, 2016.
http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Aggarwal, C.C. (2018). Machine Learning for Text: An Introduction. In: Machine Learning for Text. Springer, Cham. https://doi.org/10.1007/978-3-319-73531-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-73531-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73530-6
Online ISBN: 978-3-319-73531-3
eBook Packages: Computer ScienceComputer Science (R0)