IDEAS home Printed from
   My bibliography  Save this article

Word Cloud and Sentiment Analysis of Amazon Earphones Reviews with R Programming Language


  • Ahmed Imran KABIR
  • Koushik AHMED
  • Ridoan KARIM


The internet has opened a very wide range of ways for exchanging information or data. The development of internet influenced our daily lives to share our opinion on internet. We can share our opinions in social media like twitter, Facebook, LinkedIn or micro blogging site. We can give reviews about any product or we can share what things we are expecting. The sharing of information in internet makes internet a rich resource. Large organization or any type of business who want to do business in a customer centric way needs to know what peoples are thinking. To know that, we can use online resources but to analyze all the data in a short time is not easy if we try to figure out everyone is thought one by one. Sentiment analysis and word cloud in text mining is introduced to eradicate this problem. It helps to know what peoples are thinking and helps to develop the client experience and helps to take decision in a customer centric way. The project on word cloud and sentiment analysis of amazon earphones reviews is basically done to know the process which we can used in our practical life to know the people’s attitude, opinions, reviews, sentiment towards something from unstructured big data from online resources.

Suggested Citation

  • Ahmed Imran KABIR & Koushik AHMED & Ridoan KARIM, 2020. "Word Cloud and Sentiment Analysis of Amazon Earphones Reviews with R Programming Language," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 24(4), pages 55-71.
  • Handle: RePEc:aes:infoec:v:24:y:2020:i:4:p:55-71

    Download full text from publisher

    File URL:,%20ahmed,%20karim.pdf
    Download Restriction: no


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

    Cited by:

    1. Ahmed Imran KABIR & Suraya AKTER & Sriman MITRA, 2021. "Students Engagement Detection in Online Learning During Covid-19 Pandemic Using R Programming Language," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 25(3), pages 26-37.


    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:aes:infoec:v:24:y:2020:i:4:p:55-71. 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: Paul Pocatilu (email available below). General contact details of provider: .

    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.