Brand PR Risk Monitor: An Automated Real-Time Sentiment Analysis System for Corporate Reputation Management

Abstract

In the modern digital landscape, a single negative social media post can escalate into a full-scale corporate crisis within hours. This project develops an automated Brand PR Risk Monitor designed to safeguard corporate reputation through real-time sentiment detection and data -driven insights. Using a dataset of over 70,000 social media mentions, the system employs a Support Vector Machine (SVM) algorithm optimized with TF-IDF Vectorization and Bigram analysis. By focusing on the linguistic context of brand mentions, the model achieves a high classification accuracy of 83%, effectively distinguishing between standard customer feedback and high-stakes PR threats. The project features a central Streamlit Dashboard providing a live Risk Scanner for immediate High Risk or Safe alerts, and a Historical Analytics module that identifies brands facing the highest volume of negative sentiment, enabling proactive intervention. This system transforms messy social media data into an actionable early-warning system, enabling companies to move from reactive damage control to proactive brand protection.

Introduction

The text describes a machine learning-based system called a Brand PR Risk Monitor designed to help organizations track and manage brand reputation across social media platforms like Twitter, Reddit, and Facebook. It addresses the challenge of rapidly growing online conversations where negative posts can quickly go viral and damage brand value before human PR teams can respond.

The system uses a Support Vector Machine (SVM) model trained on over 70,000 labeled social media posts to classify sentiment into Positive, Negative, or Neutral with about 83% accuracy. It processes text using NLP techniques such as TF-IDF vectorization with bigrams, cleaning, tokenization, and stop-word removal to better understand context in social media language. The processed data is then visualized through a Streamlit-based dashboard, allowing real-time monitoring of brand sentiment trends and risk alerts.

The main problem addressed is the inefficiency of manual monitoring systems, which cannot handle large volumes of social media data or detect fast-spreading negative sentiment in real time. Existing approaches often rely on keyword matching, which fails to understand context, sarcasm, or slang, leading to inaccurate results and delayed responses.

The proposed system automates this process by continuously analyzing social media mentions, identifying high-risk negative posts, and presenting insights through interactive charts and alerts. This enables PR teams to shift from reactive damage control to proactive crisis prevention.

The system architecture includes a data processing pipeline, an SVM-based sentiment classifier, and a visualization dashboard. It uses Python, Scikit-learn, Pandas, and Streamlit for implementation. Data is stored and processed efficiently, while visualization tools like Plotly present sentiment distribution and brand risk levels in an easy-to-understand format using color-coded indicators (red, yellow, green).

Conclusion

This project successfully developed an end-to-end Brand PR Risk Monitor capable of processing over 70,000 social media records with an 83% classification accuracy. By implementing a Support Vector Machine trained on TF-IDF Bigram features, the system reliably identifies public dissatisfaction and potential brand crises, demonstrating that automated machine learning can serve as a viable replacement for manual social media monitoring. The seamless integration of the Data Layer (cleaning and vectorization), Application Layer (SVM-based risk logic), and Presentation Layer (Streamlit dashboard) proves that complex AI can be packaged into an intuitive, non-technical tool — empowering PR managers to monitor brand health without requiring machine learning expertise. Most importantly, this system achieves the fundamental objective of shifting corporate PR management from reactive damage control to proactive brand protection. Negative trends are flagged within seconds of occurrence, data-driven sentiment scores replace guesswork, and interactive visualizations reduce analysis time from days to minutes. The Brand PR Risk Monitor represents a practical and cost-effective solution for any organization operating in today\'s high-velocity social media landscape.

References

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Copyright

Copyright © 2026 Dr. K. Sundeep Kumar, M. Navalan, K. Sasikumar, C. Balasyam, D. N. Rakesh, A. Ganesh, C. Keerthi. 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.