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Leveraging Fine-Grained Sentiment Analysis for Competitivity

Author

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  • Stephen Nabareseh

    (Faculty of Management and Economics, Tomas Bata University in Zlin, Czech Republic†Ghana Revenue Authority, Accra, Ghana)

  • Eric Afful-Dadzie

    (#x2021;University of Ghana Business School, University of Ghana, Legon, Ghana)

  • Petr Klimek

    (Faculty of Management and Economics, Tomas Bata University in Zlin, Czech Republic)

Abstract

The surge in the use of social media tools by most businesses and corporate society for varied purposes cannot be over emphasised. The two top social media sites heavily patronised by businesses are Facebook and Twitter. For companies to harness the business potential of social media to increase competitive advantage, sentiments behind textual data of their customers, fans and competitors must be monitored and analysed with keen interest. This paper demonstrates how companies in the Telecommunication industry can understand consumer opinions, frustrations and satisfaction through opinion mining analyses and interpret customers’ textual data to enhance competitiveness. Sentiment analysis that classifies positive, negative and neutral sentiments of customers of the top three telecommunication companies in Ghana (MTN, Vodafone and Tigo) is studied. The proposed method extracts “intelligence” from the classified customers’ comments and compares it with responses from the companies. The results show how customer sentiments can be harnessed into successful online advertising projects. Companies can use the results to enhance their responsiveness to customer-centred, improve on the quality of their service, integrate social sentiments into PR plan, develop a strategy for social media marketing and leverage on the advantages of online advertising.

Suggested Citation

  • Stephen Nabareseh & Eric Afful-Dadzie & Petr Klimek, 2018. "Leveraging Fine-Grained Sentiment Analysis for Competitivity," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-20, June.
    • Handle: RePEc:wsi:jikmxx:v:17:y:2018:i:02:n:s0219649218500181
    DOI: 10.1142/S0219649218500181
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    References listed on IDEAS

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    Cited by:

    1. Madan Lal Yadav & Basav Roychoudhury, 2019. "Effectiveness of Domain-Based Lexicons vis-à-vis General Lexicon for Aspect-Level Sentiment Analysis: A Comparative Analysis," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 1-18, September.

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