Reputation Analysis of Companies and CEOs#
Analyzing the Dynamic Relationship between CEO and Company Reputation: A Stochastic Process Theory Approach
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.
Research questions#
How effective is the proposed approach for modeling statistical dynamics of reputation in different industry sectors and types of companies?
Can the proposed approach be extended to analyze sentiment evolution over time for other types of entities such as products, services, or individuals?
How accurate is the separation of CEO and company sentiment scores using the proposed approach compared to other existing methods?
Can the proposed approach be applied to analyze the sentiment dynamics of companies and CEOs in different countries and cultures?
How reliable is the anomaly detection technique using topic modeling in identifying major announcements or events that affect the reputation of a company and its CEO?
What are the specific topics or events that have a significant impact on the reputation of a company and its CEO, and how can this information be used to inform reputation management strategies?
What are the causal relationships between the sentiment of a company and its CEO, and how can this information be used to improve the reputation of the company and its management?
Can the proposed approach be used to predict the future sentiment of a company and its CEO based on historical sentiment data and the identification of significant events or topics?
Literature Review#
Reputation research is a multidisciplinary field that examines the perception and evaluation of individuals, organizations, and brands by stakeholders. In today’s digital age, measuring the reputation of a company and its CEO is of great importance in understanding how the public perceives them and how they evolve over time. Traditionally, reputation research has relied on surveys and other forms of direct data collection to measure the reputation of a company or brand. However, with the growth of social media and other digital platforms, researchers and practitioners have increasingly turned to big data and computational methods to measure reputation.
Dutton and Dukerich [1991] article explores the interplay between an organization’s image and its identity and how this dynamic can affect the organization’s ability to adapt to changes in its environment. Deephouse [2000]’s article examines the role of media reputation in organizational strategy, highlighting the importance of managing media reputation as a valuable resource that can contribute to a positive reputation and competitive advantage over time. Rindova and Fombrun [1999]’s article proposes a theoretical framework that suggests that reputation is constructed through the interactions between the organization and its constituents, including customers, employees, suppliers, and regulators.
Gioia et al. [2000]’s article provides a theoretical foundation for understanding the relationship between an organization’s identity, image, and adaptive instability. Their work highlights the importance of developing a strong and coherent identity and image to maintain a positive reputation and competitive advantage over time. Moreover, their work suggests that adaptive instability is a key component of reputation management and organizational success.
Barnett et al. [2006]’s article provides a comprehensive overview of the literature on corporate reputation and offers a framework for understanding this complex construct. Their work highlights the multidimensional nature of reputation and its importance for organizational success and stakeholder relationships. Bromley [1993]’s article explores the role of stakeholder behavior in shaping corporate reputation, emphasizing the importance of understanding the interests and values of stakeholders and their impact on organizational reputation over time.
Fombrun and Van Riel [1997] provides a comprehensive framework for understanding corporate reputation. The authors argue that reputation is a multidimensional construct that reflects the perceptions of stakeholders about an organization’s past behavior, current actions, and future prospects. The article identifies six dimensions of reputation: products and services, financial performance, innovation, workplace, citizenship, and governance. The authors also explore the antecedents and consequences of reputation, including its impact on stakeholder relationships, financial performance, and regulatory outcomes.
Fombrun et al. [2000] proposes a method for measuring corporate reputation that incorporates the perspectives of multiple stakeholders, including customers, employees, investors, and regulators. The authors introduce the Reputation Quotient (RQ), a metric that assesses reputation across seven dimensions: products and services, financial performance, workplace, social responsibility, vision and leadership, emotional appeal, and competitive advantage. The article also explores the relationship between reputation and financial performance, suggesting that companies with higher RQ scores tend to outperform their peers over time.
These seminal works provide a foundation for understanding the complexity and multidimensionality of reputation and highlight the importance of reputation management for organizational success and stakeholder relationships. They also suggest that traditional methods of measuring reputation may need to be supplemented with big data and computational methods to capture the complexity and nuances of reputation in today’s digital age.
Colleoni et al. [2011] propose a method for monitoring corporate reputation using real-time sentiment analysis of social media data. The authors argue that social media platforms have both increased the strategic importance of managing corporate reputation and made it more difficult to control. They analyze user-generated content to measure and monitor the feelings, opinions, and sentiments of people about a company. The authors find that their approach provides a useful and efficient method for monitoring corporate reputation in real-time. The platform can capture and analyze large volumes of social media data and provide insights into the dynamics and trends of reputation over time.
Chung et al. [2019] investigates the evolution of online sentiments towards a company during a crisis and the effects of corporate apology on those sentiments. The study uses a large dataset of over 2.6 million tweets about a food poisoning case of a company, employing a supervised machine learning approach for sentiment polarity classification and relevance classification. The findings reveal that the overall sentiment of tweets specific to the crisis was neutral, and that corporate crises draw public attention and spark public discussion on social media. The study also found that while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety. The study offers valuable insights for researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.
Cherchiello [2011] proposes a statistical approach to measuring corporate reputation using textual data. The author suggests that traditional survey-based measures of reputation may be costly and time-consuming, and may not capture the full complexity of reputation in today’s digital age. To address this issue, the author proposes a statistical model that combines text mining and machine learning techniques to measure reputation using textual data from sources such as social media, news articles, and corporate reports. The author tests the model using data from the Reputation Institute’s Global RepTrak 100, a survey-based measure of corporate reputation, and finds that the model’s predictions are highly correlated with the survey-based measure. The author suggests that the statistical model provides a more efficient and accurate method for measuring reputation than traditional survey-based methods. The model can be used to analyze large volumes of textual data and capture the nuances and complexities of reputation that may be missed by survey-based measures.
Loke and Reitter [2021] propose a machine learning-driven approach to measuring corporate reputation using online reviews. The authors argue that traditional survey-based methods of measuring corporate reputation can be time-consuming and costly, and may not capture the full complexity of reputation. To address this issue, the authors use aspect-based sentiment analysis (ABSA) to analyze online reviews and identify specific aspects of a company’s reputation, such as customer service or product quality. The authors use machine learning techniques to conduct the analysis in an unsupervised manner, without the need for manual labeling of data. The authors find that their approach provides a fine-grained analysis of corporate reputation that can help organizations identify strengths and weaknesses and make improvements. The approach is validated and evaluated using Trustpilot review data.
O'connor et al. [2010] explores the relationship between social media sentiment and public opinion polls. By analyzing surveys on consumer confidence and political opinion, and measuring sentiment word frequencies in contemporaneous Twitter messages, the authors found correlations as high as 80% that capture important large-scale trends. The study highlights the potential of text streams as a substitute and supplement for traditional polling, and shows that temporal smoothing is a critically important issue for building a successful model. Overall, the paper demonstrates the value of using sentiment analysis on social media to track public opinion in a timely and cost-effective way.
Nguyen et al. [2012] discuss the importance of analyzing social media time-series data to predict changes in public opinion. By doing so, businesses and government agencies can respond to negative sentiment and take steps to improve their public image. The authors propose a strategy for building statistical models that predict collective sentiment dynamics without analyzing individual tweets or users. Their experiments on large-scale Twitter data showed that the model achieved over 85% accuracy in predicting directional sentiment. Overall, this paper provides valuable insights for stakeholders on how to leverage social media to address and proactively manage public opinion.
Rodriguez-ibanez et al. [2020] conducted an experimental study to investigate the statistical and temporal dynamics of sentiment analysis, which is widely used to measure emotional information from social media. The authors analyzed the short text messages of six user groups with different characteristics, including universities, politics, musicians, communication media, technological companies, and financial companies. They compiled messages from the last two weeks of ten high-intensity users in each group and processed them for sentiment scoring using different lexicons. The study found that lexicons had a moderate dynamic range, and their end-of-scale resolution was low. The analysis showed that seasonal patterns were more present in the time evolution of the number of tweets, but to a lesser extent in the sentiment intensity. The authors developed the Compounded Aggregated Positivity Index, which proved to be characteristic for industries and helped to identify singularities among peers. The study concludes that temporal properties of messages provide information about the sentiment dynamics, which varies in terms of lexicons and users, but commonalities can be exploited using appropriate temporal digital processing tools.
Several studies have explored the relationship between reputation and financial performance. For example, Roberts and Dowling [2002] examines the relationship between corporate reputation and financial performance. The authors use a sample of firms from the Fortune 500 list to investigate whether a firm’s reputation can lead to sustained superior financial performance. The study uses a combination of secondary data and surveys to measure both reputation and financial performance. The results show that there is a positive relationship between a firm’s reputation and its financial performance, particularly in terms of return on assets and return on investment. The authors suggest that a strong reputation can lead to increased customer loyalty, higher sales, and lower costs, which can ultimately result in superior financial performance. However, the authors also note that a good reputation alone is not enough, as it needs to be accompanied by effective management and a focus on strategic goals to truly lead to sustained superior financial performance.
Methodology#
Modeling Statistical Dynamics of Reputation#
In the study of reputation, we propose a signal model to analyze sentiment evolution over time. Let \(S(t, \phi)\) denote the stochastic process that defines the time evolution of sentiment for a population of \(A\) articles with respect to some topic \(\phi\). The observable fluctuations of this sentiment evolution can be represented as \(s(t,f)\), which is obtained by sampling at a set of times \(t_j\) and possibly at different news articles. Mathematically, we can write:
Here, \(N_a\) is the number of articles, and \(s_j(f)\) is the sentiment score of article \(j\) with respect to the topic \(f\). We can assume that the sentiment score is obtained through a mathematical transformation of the text content of the article, denoted by operator \(\Gamma\), so we can write:
Here, \(a_j\) is the text content of article \(j\), and \(l\) is the lexicon that the operator is linked to. The time instant \(t_j\) corresponds to the time the article is published, and the sentiment scores can be obtained by applying the operator \(\Gamma\) to the article content using the lexicon \(l\).
To analyze the statistical and temporal dynamics of sentiment analysis, we use Stochastic Process Theory principles to define the time evolution of sentiment for a population of \(A\) articles with respect to a topic \(\phi\). The observable fluctuations in sentiment, \(s(t,f)\), are obtained by sampling at set times \(t_j\) and different articles. The sampling intensity per time window can be defined as:
where \(T_n \equiv ((n-1)T_w, nT_w)\), \(T_w\) is the time integration window, and \(n\) is the time window index. An average sentiment measurement and standard deviation per time window can be obtained as follows:
The M-mode representation is used to represent the multidimensional signal of several topics simultaneously. We can define the M-mode representation of the sentiment measurement signals as:
where \(x_{j,f}(t)\) is the non-sampled sentiment measurement signal of article \(j\) with respect to topic \(f\). If \(x_j(t+\tau)\) represents its replica displaced by \(\tau\) time units, then the autocorrelation function of the sentiment measurement signals is defined as:
These elements describe Stochastic Process Theory principles for sentiment analysis based on article measurements.
The probability density function (pdf) of a discrete-time random process \(s[n]\) is important as it quantifies the certainty and uncertainty of the results obtained in each realization. In this study, the pdf is defined as:
where \(\hat{s}_j(f)\) represents the set of sentiment scores for each article \(a_j\) related to entity \(f\). The independent variable of all possible score values is represented by \(\rho\). The value of this function is positive throughout the domain and represents the overall distribution of its statistical density aggregated through time and therefore independently from it.
To evaluate the amount of information attached to an entity and the cross-dependence of two paired random variables, the entropy (H) and mutual information (MI) will be used. The entropy is expressed as:
Here, \(P(f,\rho)\) is the discrete probability density function of the score \(\rho\) for entity \(f\). The entropy is a one-dimensional array of the differential H evaluated in the time slot under study for every considered entity.
The mutual information is defined for discrete random variables and is given by:
Here, \(\hat{s}_u\) and \(\hat{s}_v\) are two random variables corresponding to two entities measured for a given lexicon and sentiment analysis method. \(N\) and \(M\) represent the number of states for \(\hat{s}_u\) and \(\hat{s}_v\), respectively. The mutual information is symmetric in \(\hat{s}_u\) and \(\hat{s}_v\), always positive, and is equal to zero if and only if \(\hat{s}_u\) and \(\hat{s}_v\) are independent.
In summary, the presented signal model and notation allow for a straightforward analysis of sentiment evolution over time in a population of articles related to a certain topic. The use of Stochastic Process Theory principles and probability density functions enable the quantification of the certainty and uncertainty of the results obtained in each realization. The evaluation of entropy and mutual information provides measures of the amount of information attached to an entity and the cross-dependence of two paired random variables, respectively.
Appying the Model to Separate the CEO and Company#
To apply this model to separate the reputation of a company and its CEO, we need to first define the entities and sentiment scores for each. We can consider the company and CEO as separate entities and use the sentiment scores of articles mentioning each entity to calculate their respective reputations.
Let’s denote the sentiment score of articles mentioning the company and CEO as \(s_c(t)\) and \(s_{ceo}(t)\), respectively. We can calculate the sentiment score of each entity for a specific time interval by averaging the sentiment scores of the articles mentioning that entity during that interval. This gives us the following expressions:
Here, \(N_c\) and \(N_{ceo}\) are the number of articles mentioning the company and CEO, respectively, during the time interval \(t\), and \(f_c\) and \(f_{ceo}\) are the topics corresponding to the company and CEO, respectively.
We can then calculate the reputation of the company and CEO by taking the average sentiment score over a longer time period, say a month. Let’s denote this time period as \(T\) and the reputation of the company and CEO over this time period as \(R_c\) and \(R_{ceo}\), respectively. We can calculate these reputations as follows:
These expressions give us the average sentiment score of the articles mentioning the company and CEO over a longer time period, which can be used as a measure of their respective reputations.
The mutual information (MI) between the sentiment scores of a company and its CEO measures the amount of information that can be obtained about the sentiment of the company by knowing the sentiment of its CEO, and vice versa. If the MI between the two is high, it suggests a strong relationship between the sentiment of the company and its CEO.
Additionally, we can perform further analysis on the sentiment scores to determine if there are any specific topics or events that are driving the sentiment of the company and its CEO. This could provide insights into the factors that are influencing the relationship between the two and help inform strategies for managing reputation and public perception.
Causal Relationship between the Company and CEO#
While mutual information can reveal the relationship between two variables, it does not necessarily indicate a causal relationship between them. Correlation or mutual information alone cannot establish causality. Establishing causality involves tracking changes in the CEO’s sentiment score before and after a major announcement or event that affects the company’s reputation. If changes in the CEO’s sentiment score precede changes in the company’s sentiment score, it may suggest a causal relationship where the CEO’s actions or statements influence the company’s reputation.
We can use anomaly detection techniques to find events that significantly deviate from the expected sentiment of the company and its CEO, indicating a potential shift in the company’s reputation. Anomaly detection is the process of identifying patterns or observations that deviate significantly from the norm or expected behavior. With topic modeling, we can identify topics or clusters of documents that contain unexpected or rare content compared to the overall corpus.
To detect anomaly using topic modeling, we can apply unsupervised techniques such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) to the text corpus and identify topics that are infrequent or dissimilar to the rest of the corpus. These topics are then flagged as anomalies.
Implementation#
Data Collection and Preprocessing#
We will collect a large volume of public texts such as news articles, social media posts, and reviews that are relevant to the company and CEO. Once we have obtained the news articles, we need to preprocess the data to make it suitable for topic modeling.
The following steps outline the process for preprocessing the data:
Text cleaning: This involves removing any unwanted characters, such as HTML tags or punctuation marks, and converting the text to lowercase.
Tokenization: This involves breaking the text into individual words or tokens.
Stop word removal: Stop words are common words that do not carry much meaning, such as “the”, “a”, and “is”. Removing these words can help to reduce noise in the data.
Stemming or lemmatization: This involves reducing words to their base form, such as converting “running” to “run”, or “am” to “be”. This can help to reduce the number of unique words in the data and improve the efficiency of topic modeling.
Removing rare or common words: Words that occur very frequently or very infrequently in the data may not be useful for topic modeling and can be removed.
CEO and Company Separation#
To identify relevant articles, we first extract articles from news sources that are likely to report on the company and its CEO. To separate the articles that mention the company from those that mention the CEO, we use NER to identify the entities in the articles, followed by coreference resolution to link pronouns to the entities they refer to. We then use topic modeling to identify the topics discussed in the articles related to the company and the CEO. If an article discusses both the company and the CEO, we use the technique of section separation to separate the sections of the article that discuss the company and the CEO whenever it’s possible.
Aspect Based Sentiment Analysis#
Aspect-based sentiment analysis (ABSA) is a technique that is used to identify and extract the sentiment associated with specific aspects or attributes of a product, service, or entity. In our case, ABSA can be applied to extract sentiment associated with the CEO and the company separately. To perform ABSA, we first need to identify the specific aspects or attributes that we want to analyze. In the case of a company, these aspects could include product quality, customer service, financial performance, and so on. For the CEO, these aspects could include leadership style, communication skills, decision-making abilities, and so on.
Once we have identified the aspects, we can then use natural language processing (NLP) techniques to extract and analyze the sentiment associated with each aspect. This can involve techniques such as part-of-speech tagging, named entity recognition, and sentiment analysis. We can use NLP tools to extract all the adjectives or verbs associated with each aspect for the CEO and the company separately. We can then use sentiment analysis techniques to classify each adjective or verb as positive, negative, or neutral. This will give us a sentiment score for each aspect, indicating whether it is generally perceived positively or negatively.
The output of ABSA will be sentiment scores for each aspect, allowing us to identify which aspects are driving positive or negative sentiment for the CEO and the company separately. This information can then be used to make informed decisions and take appropriate actions to improve the reputation of the CEO and the company.
Aggregating Sentiment Scores#
To calculate the aggregate sentiment scores for the company and CEO, we can choose any time frame, such as daily, weekly, or monthly, and use various weighting schemes. We can also apply exponential smoothing to the sentiment scores to better capture the trends and suppress noise in the data.
In this implementation, we calculate the average sentiment score for each entity over a daily time frame. However, other time frames can be used depending on the needs of the analysis. We apply a simple weighting scheme where all articles are given equal weight. Other weighting schemes, such as giving more weight to influential sources or recent articles, can also be applied depending on the analysis objectives.
We can also apply exponential smoothing to the sentiment scores to better capture the trends and suppress noise in the data. Exponential smoothing is a widely used method for time series analysis and forecasting, and it involves giving more weight to recent observations while gradually decreasing the weights of older observations. This approach can provide more accurate and reliable aggregate sentiment scores, especially when dealing with noisy and volatile data.
We will follow the steps below:
Aggregate the sentiment scores of each aspect/category for both the CEO and the company from the ABSA results.
Calculate the sentiment score for each article as the weighted average of the aspect/category scores based on their importance in the article. The importance can be determined by the frequency of the aspect/category in the article or by using a more advanced technique such as TF-IDF.
Use the sentiment scores of the articles to calculate the sentiment time series for both the CEO and the company using the same approach as described in the “Modeling Statistical Dynamics of Reputation” section earlier.
Anomaly Detection#
Anomaly detection can be implemented using topic modeling techniques. In this approach, the objective is to detect topics that deviate from the normal patterns in the sentiment scores of the CEO and the company.
To implement this, we first need to generate topic models for the sentiment scores of the CEO and the company, separately. We can use techniques such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) for topic modeling. These techniques will identify the topics present in the sentiment scores, and assign probabilities of each topic to each sentiment score.
Once we have the topic models, we can calculate the topic distribution of the sentiment scores for a given time period. We can then compare the topic distribution of the current time period with the topic distribution of the historical periods to detect any anomalies.
A commonly used technique for anomaly detection is to use the Mahalanobis distance metric. The Mahalanobis distance is a statistical distance metric that measures the distance between a point and a distribution. In this context, we can use the Mahalanobis distance to measure the distance between the current topic distribution and the historical topic distribution. If the Mahalanobis distance exceeds a certain threshold, we can consider the current topic distribution as an anomaly.
We can also use clustering techniques, such as k-means clustering or hierarchical clustering, to cluster the sentiment scores based on their topic distribution. We can then identify the clusters that deviate from the normal patterns as anomalies.
Causal Analysis#
Once we have detected events that have impacted the reputation of the company or its CEO using topic modeling and anomaly detection techniques, we can further investigate the causal relationship between the two entities.
One approach is to use Granger causality analysis, which is a statistical test to determine whether one time series can be used to predict another time series. In our case, we can use the sentiment scores of the company and CEO as two time series and test whether the sentiment scores of one entity can predict the sentiment scores of the other entity.
We can implement Granger causality analysis using the statsmodels package in Python. First, we can split the sentiment scores into two time series for the company and CEO. Then, we can run the Granger causality test.
Alternatively, we can use other causal inference techniques such as regression analysis, instrumental variable analysis, or structural equation modeling to investigate the causal relationship between the two entities. The choice of technique depends on the specific research question and data characteristics.
Expected Outcomes#
The expected outcomes of this research have significant implications for various stakeholders, including companies, investors, and policymakers.
For companies, the implementation of this approach can provide insights into their reputation dynamics and help identify potential risks and opportunities. Companies can monitor their reputation in real-time, which can help them take proactive measures to mitigate negative impacts and capitalize on positive ones. Additionally, this approach can provide a more comprehensive understanding of the relationship between a company and its CEO, which can help companies make informed decisions about their leadership and communication strategies.
For investors, this approach can provide a more reliable and accurate measurement of a company’s reputation, which can help investors make informed investment decisions. Investors can use this approach to monitor the reputation of the companies in their portfolio and identify potential risks that may affect their investments.
For policymakers, this approach can help monitor the reputation of companies in different industries and identify potential risks to the economy. Policymakers can use this approach to monitor the reputation of companies in industries that are critical to the economy, such as financial services and healthcare.
Now, let’s dive into the specific use cases for each stakeholder:
For companies:
Monitor their reputation in real-time and take proactive measures to mitigate negative impacts and capitalize on positive ones
Identify potential risks and opportunities related to their reputation
Improve their communication and leadership strategies based on a more comprehensive understanding of the relationship between the company and its CEO
Benchmark their reputation against competitors in the same industry
For investors:
Make informed investment decisions based on a more reliable and accurate measurement of a company’s reputation
Monitor the reputation of the companies in their portfolio and identify potential risks that may affect their investments
Identify potential investment opportunities based on the reputation dynamics of a company
For policymakers:
Monitor the reputation of companies in different industries and identify potential risks to the economy
Implement policies to mitigate the negative impacts of reputation risks on the economy
Identify industries that require regulatory attention based on the reputation dynamics of companies in those industries
Conclusion#
This research proposes a novel approach for measuring the reputation of a company and its CEO using topic modeling and aspect-based sentiment analysis. We show how this approach can reveal the causal relationship between the reputation of the company and its CEO, and can also detect anomalies in the reputation signals.
The implementation of this approach involves data collection and preprocessing, CEO and company separation, aspect-based sentiment analysis, aggregation of sentiment scores using Stochastic Process Theory principles, anomaly detection using topic modeling, and causal analysis based on previously detected events using topic modeling.
Expected outcomes of this research include improved reputation management for companies and CEOs, better decision-making for investors, more accurate risk assessment for insurers, and improved public relations for media outlets.
References#
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