Article
Version 1
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Social Media Sentiment Analysis for Brand Reputation Management
Version 1
: Received: 31 July 2024 / Approved: 1 August 2024 / Online: 2 August 2024 (05:14:46 CEST)
How to cite: Bell, C.; Olukemi, A.; Broklyn, P. Social Media Sentiment Analysis for Brand Reputation Management. Preprints 2024, 2024080029. https://doi.org/10.20944/preprints202408.0029.v1 Bell, C.; Olukemi, A.; Broklyn, P. Social Media Sentiment Analysis for Brand Reputation Management. Preprints 2024, 2024080029. https://doi.org/10.20944/preprints202408.0029.v1
Abstract
In the digital age, social media platforms have become a critical channel for brands to engage with their audience and manage their reputation. This paper explores the application of sentiment analysis in social media to aid brand reputation management. Sentiment analysis, a subset of natural language processing (NLP), involves determining the emotional tone behind online discussions and opinions. By leveraging machine learning algorithms and NLP techniques, brands can analyze vast amounts of social media data to gauge public sentiment, identify emerging trends, and detect potential issues before they escalate. This study reviews current methodologies in sentiment analysis, including supervised and unsupervised learning approaches, and discusses their effectiveness in extracting actionable insights from social media content. Additionally, it examines case studies where sentiment analysis has been successfully implemented to enhance brand reputation, address customer concerns, and tailor marketing strategies. The findings highlight the importance of continuous monitoring and adaptation to dynamic social media landscapes for maintaining a positive brand image. This paper provides a comprehensive overview of sentiment analysis tools and techniques, offering practical recommendations for brands seeking to leverage social media sentiment data for proactive reputation management.
Keywords
social media sentiment; data proactive; reputation management
Subject
Business, Economics and Management, Marketing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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