IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i2p290-297.html
   My bibliography  Save this article

Kernel-based machine learning for fast text mining in R

Author

Listed:
  • Karatzoglou, Alexandros
  • Feinerer, Ingo

Abstract

Recent advances in the field of kernel-based machine learning methods allow fast processing of text using string kernels utilizing suffix arrays. kernlab provides both kernel methods' infrastructure and a large collection of already implemented algorithms and includes an implementation of suffix-array-based string kernels. Along with the use of the text mining infrastructure provided by tm these packages provide R with functionality in processing, visualizing and grouping large collections of text data using kernel methods. The emphasis is on the performance of various types of string kernels at these tasks.

Suggested Citation

  • Karatzoglou, Alexandros & Feinerer, Ingo, 2010. "Kernel-based machine learning for fast text mining in R," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 290-297, February.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:2:p:290-297
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00353-3
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    2. Usotskaya, N. & Ryabko, B., 2009. "Application of information-theoretic tests for the analysis of DNA sequences based on Markov chain models," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1861-1872, March.
    3. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grübler, Julia & Reiter, Oliver, 2021. "Characterising non-tariff trade policy," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 138-163.
    2. Mahdi Ghodsi & Julia Grübler & Oliver Reiter & Robert Stehrer, 2017. "The Evolution of Non-Tariff Measures and their Diverse Effects on Trade," wiiw Research Reports 419, The Vienna Institute for International Economic Studies, wiiw.
    3. Nixon N. Nduji & Christian N. Madu & Chukwuebuka C. Okafor, 2022. "A Low-Cost Web Application System for Monitoring Geometrical Impacts of Surface Subsidence," Sustainability, MDPI, vol. 14(21), pages 1-19, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tsukioka, Yasutomo & Yanagi, Junya & Takada, Teruko, 2018. "Investor sentiment extracted from internet stock message boards and IPO puzzles," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 205-217.
    2. Grinis, Inna, 2017. "The STEM requirements of "non-STEM" jobs: evidence from UK online vacancy postings and implications for skills & knowledge shortages," LSE Research Online Documents on Economics 85123, London School of Economics and Political Science, LSE Library.
    3. Julia Bachtrögler & Christoph Hammer & Wolf Heinrich Reuter & Florian Schwendinger, 2019. "Guide to the galaxy of EU regional funds recipients: evidence from new data," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(1), pages 103-150, February.
    4. Daniel J. Luckett & Eric B. Laber & Samer S. El‐Kamary & Cheng Fan & Ravi Jhaveri & Charles M. Perou & Fatma M. Shebl & Michael R. Kosorok, 2021. "Receiver operating characteristic curves and confidence bands for support vector machines," Biometrics, The International Biometric Society, vol. 77(4), pages 1422-1430, December.
    5. Grabisch, Michel & Kojadinovic, Ivan & Meyer, Patrick, 2008. "A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package," European Journal of Operational Research, Elsevier, vol. 186(2), pages 766-785, April.
    6. Shuyue Huang & Lena Jingen Liang & Hwansuk Chris Choi, 2022. "How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    7. Laura Anderlucci & Cinzia Viroli, 2020. "Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 759-770, December.
    8. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    9. Stefano Sbalchiero & Maciej Eder, 2020. "Topic modeling, long texts and the best number of topics. Some Problems and solutions," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(4), pages 1095-1108, August.
    10. Riza, Lala Septem & Bergmeir, Christoph & Herrera, Francisco & Benítez, José M., 2015. "frbs: Fuzzy Rule-Based Systems for Classification and Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i06).
    11. Karin Wolffhechel & Amanda C Hahn & Hanne Jarmer & Claire I Fisher & Benedict C Jones & Lisa M DeBruine, 2015. "Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-10, October.
    12. Daoud, Adel & Kohl, Sebastian, 2016. "How much do sociologists write about economic topics? Using big data to test some conventional views in economic sociology, 1890 to 2014," MPIfG Discussion Paper 16/7, Max Planck Institute for the Study of Societies.
    13. Necmettin Alpay Koçak, 2020. "The Role of Ecb Speeches in Nowcasting German Gdp," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2020(2), pages 05-20.
    14. JooSeok Oh & Timothy Paul Connerton & Hyun-Jung Kim, 2019. "The Rediscovery of Brand Experience Dimensions with Big Data Analysis: Building for a Sustainable Brand," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    15. Schierholz, Malte & Gensicke, Miriam & Tschersich, Nikolai, 2016. "Occupation coding during the interview," IAB-Discussion Paper 201617, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    16. Igor Barahona & Daría Micaela Hernández & Héctor Hugo Pérez-Villarreal & María Pilar Martínez-Ruíz, 2018. "Identifying research topics in marketing science along the past decade: a content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 293-312, October.
    17. Sangsung Park & Sunghae Jun, 2020. "Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis," Sustainability, MDPI, vol. 12(2), pages 1-11, January.
    18. David C Molik & DeAndre Tomlinson & Shane Davitt & Eric L Morgan & Matthew Sisk & Benjamin Roche & Natalie Meyers & Michael E Pfrender, 2021. "Combining natural language processing and metabarcoding to reveal pathogen-environment associations," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 15(4), pages 1-21, April.
    19. Hornik, Kurt & Grün, Bettina, 2014. "movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i10).
    20. Tomasz Kopczewski, 2015. "Think not calculate! Implementation of Felix Klein postulates in economic education with CAS software," Working Papers 2015-38, Faculty of Economic Sciences, University of Warsaw.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:54:y:2010:i:2:p:290-297. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.