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The Power of Social Media Analytics: Text Analytics Based on Sentiment Analysis and Word Clouds on R

Author

Listed:
  • Ahmed Imran KABIR
  • Ridoan KARIM
  • Shah NEWAZ
  • Muhammad Istiaque HOSSAIN

Abstract

Apparently, word clouds have grown as a clear and appealing illustration or visualization strategy in terms of text. Word clouds are used as a part of various settings as a way to give a diagram by cleansing text throughout those words that come up with most frequently. Generally, this is performed constantly as an unadulterated text outline. In any case, that there is a bigger capability to this basic yet intense visualization worldview in text analytics. In this work, we investigate the adequacy of word clouds for general text analysis errands and also analyze the tweets to find out the sentiment and also discuss the legal aspects of text mining. We used R software to pull twitter data which depends altogether on word cloud as a visualization technique and also with the help of positive and negative words to determine the user sentiment. We indicate how this approach can be viably used to explain text analysis tasks and assess it in a qualitative user research.

Suggested Citation

  • Ahmed Imran KABIR & Ridoan KARIM & Shah NEWAZ & Muhammad Istiaque HOSSAIN, 2018. "The Power of Social Media Analytics: Text Analytics Based on Sentiment Analysis and Word Clouds on R," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 22(1), pages 25-38.
    • Handle: RePEc:aes:infoec:v:22:y:2018:i:1:p:25-38
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    Citations

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    Cited by:

    1. Ahmad M. Alghamdi & Salvatore Flavio Pileggi & Osama Sohaib, 2023. "Social Media Analysis to Enhance Sustainable Knowledge Management: A Concise Literature Review," Sustainability, MDPI, vol. 15(13), pages 1-30, June.
    2. 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.
    3. Das, Subhankar & Mondal, Subhra & Puri, Vikram & Vrana, Vasiliki, 2022. "Structural review of relics tourism by text mining and machine learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 8(2), pages 25-34.
    4. Ahmed Imran KABIR & Sriman MITRA & Suraya AKTER & Md Rakibul ISLAM & Soumya Suhreed DAS, 2022. "Developing a Network Design for a Smart Airport Using Cisco Packet Tracer," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 26(1), pages 25-38.
    5. Hassan Adamu & Syaheerah Lebai Lutfi & Nurul Hashimah Ahamed Hassain Malim & Rohail Hassan & Assunta Di Vaio & Ahmad Sufril Azlan Mohamed, 2021. "Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning," Sustainability, MDPI, vol. 13(6), pages 1-21, March.

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