IDEAS home Printed from https://ideas.repec.org/a/igg/jssmet/v12y2021i6p89-104.html
   My bibliography  Save this article

Evaluation of Consumer Reviews for adidas Sports Brands Using Data Mining Tools and Twitter APIs

Author

Listed:
  • Vishu Singhvi

    (Sir Padampat Singhania University, India)

  • Prateek Srivastava

    (Sir Padampat Singhania University, India)

Abstract

The sportswear industry has become prominent, popular, and a very obvious category among various age groups in India. Big sports brands like adidas, seeing the Indian market potential, have extended their businesses across the country. To increase sales, online purchase has become one the most effective, easy, cheap, and quickest solution for the end customers as it provides the end consumers a variety of products, their designs, and color combinations on clicks. A large number of consumers express what exactly the end customer thinks about a particular product's preference level of brand, quality of service, quality of product, or stylish nature of the product. The current study does an evaluation of such online comments and reviews giving their feedback on their public Twitter accounts, flipkart.com, or amazon.in about an adidas sports brand in India. The research also provides a basic flow of Java program in the form of an algorithm that is used to collect the dataset from Twitter, process it, and export it into an Excel sheet for further investigation.

Suggested Citation

  • Vishu Singhvi & Prateek Srivastava, 2021. "Evaluation of Consumer Reviews for adidas Sports Brands Using Data Mining Tools and Twitter APIs," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 12(6), pages 89-104, November.
    • Handle: RePEc:igg:jssmet:v:12:y:2021:i:6:p:89-104
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSSMET.2021110106
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Brahami Menaouer & Abdeldjouad Fatma Zahra & Sabri Mohammed, 2022. "Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 13(1), pages 1-23, January.
    2. Jarosław Ziółkowski & Aleksandra Lęgas & Elżbieta Szymczyk & Jerzy Małachowski & Mateusz Oszczypała & Joanna Szkutnik-Rogoż, 2022. "Optimization of the Delivery Time within the Distribution Network, Taking into Account Fuel Consumption and the Level of Carbon Dioxide Emissions into the Atmosphere," Energies, MDPI, vol. 15(14), pages 1-22, July.

    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:igg:jssmet:v:12:y:2021:i:6:p:89-104. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.