| Title: | The Effectiveness of AI-Powered Sentiment Analysis in Corporate Communication in Improving Stakeholder Engagement |
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| Authors: | ID Gabelaia, Ioseb (Author)
ID Smaidziunaite, Migle (Author) |
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| Files: |  https://toknowpress.net/submission/index.php/ijmkl/article/download/204/137
 RAZ_Gabelaia_Ioseb_0.pdf (2,16 MB)
MD5: EDF10AF44399B89A852486876CD5B8DB
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | MFDPŠ - International School for Social and Business Studies
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| Abstract: | Purpose: There is limited research on Artificial intelligence and its distinctive and measurable impact on corporate communication and stakeholder engagement. Besides, existing and new research frequently lacks industry-specific insights, practical case studies, and cross-disciplinary insights. This article is the first of two articles on corporate communication and artificial intelligence developed by the authors. This article explores the effectiveness of integrating artificial intelligence into corporate communication to improve stakeholder engagement.
Study design/methodology/approach: The authors used a mixed methodology, quantitative surveys (n = 241), and qualitative interviews (n = 7) with corporate communication managers. Convenience and Snowball sampling was used.
Findings: AI-powered instruments significantly improve the capability to identify key sentiments, permitting organizations to respond proactively and maintain positive stakeholder relationships. Moreover, integrating AI in sentiment analysis improves feedback management processes, reducing response times and encouraging a more dynamic communication environment.
Originality/value: in the times of AI, this research contributes to the practical and theoretical characteristics of improving stakeholder engagement in corporate communication, and offers recommendation for actionable decisions. |
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| Keywords: | AI, corporate communication, AI-driven strategies |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 25.06.2025 |
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| Numbering: | Vol. 14, No. 1 |
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| PID: | 20.500.12556/ReVIS-12241  |
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| ISSN: | 2232-5107 |
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| eISSN: | 2232-5697 |
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| DOI: | https://doi.org/10.53615/2232-5697.14.237-248 |
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| Publication date in ReVIS: | 19.09.2025 |
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| Views: | 207 |
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| Downloads: | 8 |
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