IDEAS home Printed from https://ideas.repec.org/p/col/000559/018187.html
   My bibliography  Save this paper

Una introducción a la construcción de Word Clouds (para economistas) en R

Author

Listed:
  • Julio Cesar Alonso Cifuentes

Abstract

Ya existe una tradición reciente y creciente de emplear en múltiples disciplinas de presentar análisis de textos de manera gráfica (ver por ejemplo Baralt, Pennestri y Selvandin (2011), DeNoyelles y Reyes-Foster (2015), Heimerl, Lohmann, Lange y Ertl (2014) y Moro, Pires, Rita y Cortez (2019)). Las Word Clouds o nubes de palabras son ejemplos de cómo visualizar textos de tal manera que se pueda resumir mucha información en una gráfica. Las nubes de palabras se han convertido en una forma de iniciar análisis de textos o lo que se conoce como minería de textos.

Suggested Citation

  • Julio Cesar Alonso Cifuentes, 2020. "Una introducción a la construcción de Word Clouds (para economistas) en R," Icesi Economics Lecture Notes 18187, Universidad Icesi.
  • Handle: RePEc:col:000559:018187
    as

    Download full text from publisher

    File URL: https://www.icesi.edu.co/departamentos/images/departamentos/FCAE/economia/apuntesEconomia/Icesi_Economic_Lecture_Notes_9_2020%20.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Moro, Sérgio & Pires, Guilherme & Rita, Paulo & Cortez, Paulo, 2019. "A text mining and topic modelling perspective of ethnic marketing research," Journal of Business Research, Elsevier, vol. 103(C), pages 275-285.
    Full references (including those not matched with items on IDEAS)

    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. Hugo S. Gonçalves & Sérgio Moro, 2023. "On the economic impacts of COVID‐19: A text mining literature analysis," Review of Development Economics, Wiley Blackwell, vol. 27(1), pages 375-394, February.
    2. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    3. Despina S. Giakomidou & Athanasios Kriemadis & Dimitrios K. Nasiopoulos & Dimitrios Mastrakoulis, 2022. "Re-Engineering of Marketing for SMEs in Energy Market through Modeling Customers’ Strategic Behavior," Energies, MDPI, vol. 15(21), pages 1-20, November.
    4. Mara Cerquetti & Domenico Sardanelli & Concetta Ferrara, 2024. "Measuring museum sustainability within the framework of institutional theory: A dictionary‐based content analysis of French and British National Museums' annual reports," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(3), pages 2260-2276, May.
    5. Ozcan Saritas & Pavel Bakhtin & Ilya Kuzminov & Elena Khabirova, 2021. "Big data augmentated business trend identification: the case of mobile commerce," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1553-1579, February.
    6. Moon, Sangkil & Kim, Moon-Yong & Bergey, Paul K., 2019. "Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms," Journal of Business Research, Elsevier, vol. 102(C), pages 83-96.
    7. Chatzopoulou, Elena & Navazhylava, Kseniya, 2022. "Ethnic brand identity work: Responding to authenticity tensions through celebrity endorsement in brand digital self-presentation," Journal of Business Research, Elsevier, vol. 142(C), pages 974-987.
    8. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    9. Marco Guerzoni & Massimiliano Nuccio & Federico Tamagni, 2022. "Discovering pre-entry knowledge complexity with patent topic modeling and the post-entry growth of Italian firms," LEM Papers Series 2022/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    10. Duan, Dinglin & Gao, Zhifeng & Uddin, Md Azhar & Nian, Yefan & Nguyen, Ly, 2022. "Tracing the Trends in Consumer Preferences for Eco-labeled Food: A Text Mining and Topic Modeling Approach," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322419, Agricultural and Applied Economics Association.
    11. Rahul Kumar & Rahul Thakurta, 2021. "Exfoliating decision support system: a synthesis of themes using text mining," Information Systems and e-Business Management, Springer, vol. 19(1), pages 247-279, March.
    12. Diogo Lima & Ricardo F. Ramos & Pedro Miguel Oliveira, 2024. "Customer satisfaction in the pet food subscription-based online services," Electronic Commerce Research, Springer, vol. 24(2), pages 745-769, June.
    13. Yi, Jisu & Kim, Jongdae & Oh, Yun Kyung, 2024. "Uncovering the quality factors driving the success of mobile payment apps," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    14. Barkemeyer, Ralf & Faugère, Christophe & Gergaud, Olivier & Preuss, Lutz, 2020. "Media attention to large-scale corporate scandals: Hype and boredom in the age of social media," Journal of Business Research, Elsevier, vol. 109(C), pages 385-398.

    More about this item

    Keywords

    Word Clouds; economistas y R.;

    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:col:000559:018187. 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: Coordinador ICESI (email available below). General contact details of provider: https://edirc.repec.org/data/deiceco.html .

    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.