Introduction

In the era of digital transparency, the reputation of companies and their CEOs has become a vital driver of organizational success. Reputation, an intangible asset, influences a broad range of factors including customer loyalty, employee engagement, investor confidence, and regulatory outcomes. Thus, the need to navigate the complexities surrounding the measurement and interpretation of reputation in the current landscape is more crucial than ever.

Traditionally, reputation research was primarily grounded in surveys and direct data collection methods. With the digital revolution and the rise of social media and other digital platforms, there has been a paradigm shift towards more sophisticated, data-driven computational methodologies. These modern methodologies, harnessing the power of natural language processing and machine learning algorithms, provide nuanced insights into public sentiment towards a company or brand. Sentiment analysis, a technique commonly used to determine the sentiment polarity of textual data, has been a significant contributor to these insights. However, it often fails to capture the subtle nuances of diverse aspects that shape reputation.

One enduring challenge of existing methodologies is their inability to disentangle the company’s reputation from that of its CEO. The CEO and the company can command varying degrees of public sentiment, based on their actions and behaviours. This conflation often results in a convoluted representation of public sentiment. For instance, a company might hold a robust reputation due to superior products and services, but its CEO could be perceived negatively due to controversial personal actions. On the other hand, a CEO might be revered for visionary leadership, even if the company suffers from a poor reputation due to substandard products or services. Recognizing the significant influence that a CEO can have on a company’s reputation, and vice versa, it is vital to disentangle their respective reputations. This separation allows for a more nuanced understanding of the individual performance of the company and the CEO and helps identify potential areas of improvement. Such granular analysis enables clarity, accountability, and facilitates comparison, all crucial elements in decoding the inherent complexities of reputation interpretation.

However, the process of distinguishing between the CEO’s and the company’s reputations is fraught with challenges. The reputations of a company and its CEO are often inextricably linked, making it difficult to separate the two. Additionally, public and media narratives frequently amalgamate the two entities, complicating the task of assigning sentiments to one or the other. The dynamic nature of reputation, subject to changes in leadership, corporate scandals, shifts in public opinion, and the underlying complexity of a company’s structure and operations, further complicates this task.

In response to these challenges, an advanced technique known as Aspect-Based Sentiment Analysis (ABSA) shows promise. ABSA, founded on a pipeline of tasks such as aspect extraction, opinion target extraction, and sentiment analysis, is particularly relevant when distinguishing between the company and the CEO’s reputations. Utilizing ABSA, we propose a generative language modeling approach, using systems like GPT-4, to separately examine the reputation of a company and its CEO. This approach targets aspects closely associated with the CEO, thereby providing a more precise reputation metric for the CEO, distinct from the overall reputation of the company.

Once we have the aspect sentiments using ABSA, we propose a signal model to analyze their evolution and interplay over time for a set of articles on a particular topic. Using principles from the Stochastic Process Theory and probability density functions, the time evolution of sentiment is defined. Concepts of entropy and mutual information are utilized to quantify the information associated with an entity and the interdependence between two paired random variables.

By treating the company and its CEO as separate entities, we leverage sentiment scores from articles mentioning each entity to compute their respective reputations. We analyze the mutual information between the sentiment scores of a company and its CEO to determine the level of interdependence. Further analysis of sentiment scores can help identify key events or topics driving the sentiment of the company and its CEO. To ascertain a causal relationship, we track changes in the CEO’s sentiment score preceding significant announcements or events affecting the company’s reputation. Anomaly detection techniques such as topic modeling using unsupervised learning methodologies like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) are employed to flag deviations indicating a potential shift in the company’s reputation.

Our proposed methodology has wide-ranging potential applications. For companies, it presents a tool for reputation management, enabling the identification of risks and opportunities and informing strategic planning. Investors can utilize it to evaluate a company’s reputation and make informed decisions. Regulators can monitor companies’ reputation to ensure compliance, and media organizations can track sentiment trends to inform reporting strategies. Ultimately, the goal is to enable stakeholders to harness the power of reputation in the digital age.