In the rapidly evolving landscape of public opinion monitoring, a groundbreaking method developed by Hangyin Mao of the State Grid Zhejiang Electric Power Co., Ltd., is set to revolutionize how businesses and energy sector entities track and analyze social hotspots. Published in the esteemed journal *Measurement + Control* (translated to English as *Measurement and Automation*), Mao’s research introduces a novel approach that leverages time-series neural networks to construct a commercial map of social hotspots, offering unprecedented insights into public sentiment and event dynamics.
At the heart of Mao’s method is a three-stage process that begins with the construction of an atomic event graph. This graph captures the intricate web of connections between various events, providing a foundational framework for understanding the broader context. The second stage involves the fusion of these atomic events into a cohesive whole, while the third stage delves into public opinion analysis, uncovering the underlying trends and shifts in public sentiment.
One of the most innovative aspects of Mao’s approach is the use of unsupervised learning to train the neural network model. This allows the model to explore deep event connections without the need for labeled data, making it a powerful tool for uncovering hidden patterns and relationships. As Mao explains, “The neural network captures event dynamics and public opinion evolution, providing a comprehensive view of the social landscape.”
The method also employs an empirical mode decomposition technique to analyze Intrinsic Mode Function (IMF) data, further enhancing the understanding of intrinsic patterns within the data. Time-series neural networks are then used to classify and reconstruct event graphs, ultimately constructing a knowledge graph that offers a holistic view of the social hotspots.
The practical implications of this research are significant, particularly for the energy sector. By providing a robust and efficient way to monitor public opinion, businesses can better anticipate and respond to social hotspots, mitigating risks and capitalizing on opportunities. As Mao notes, “This method reduces storage costs, simplifies prediction, and exhibits robustness, offering new insights for commercial mapping and contributing to public opinion monitoring.”
The potential applications of this research extend beyond the energy sector, with implications for various industries that rely on public opinion monitoring. From marketing and public relations to crisis management and strategic planning, the ability to accurately track and analyze social hotspots can provide a competitive edge in an increasingly interconnected world.
As the field of public opinion monitoring continues to evolve, Mao’s research represents a significant step forward, offering a powerful tool for businesses and energy sector entities to navigate the complex social landscape. With its ability to capture event dynamics, uncover deep connections, and provide comprehensive insights, this method is poised to shape the future of public opinion monitoring and commercial mapping.