When data analysis company KeenCorp tested its own software using the email dataset, which showed communications between the company’s top 150 executives, its algorithm assigned index scores to various points in time. The lowest occurred when the company filed for bankruptcy, which made sense. But there was also a high level of tension in 1999, 30 months before the bankruptcy filing. After further investigation, these were found to be linked to the company setting up partnership companies that would mask its losses – a transaction that eventually sparked an investigation into accounting fraud and triggered Enron’s demise. What executives were prepared to say in emails clearly diverged from what they would say on the record. The former chief financial officer Andy Fastow has since said that “with the benefit of the KeenCorp Index, Enron’s board of directors would have been alerted to reconsider its decision and prevent a cultural and financial meltdown.”
This may be an extreme example, but this type of sentiment analysis can be a way of understanding employees’ moods and perceptions and to pre-empt reputational crises, gauge if an initiative is not landing well or even detect health and safety risks. The practice has been around for some time in consumer circles, combing through unstructured customer feedback and determining if it is positive or negative based on a score. Using text from multiple data sources such as emails, social media or review sites, the tools use natural language processing to break down what individuals are saying, placing the words into context and detecting whether they reflect positively or negatively on a brand. So a “sick burn” might be concerning if you sell medical supplies, but a vote of confidence in the gaming community.
In the workplace, sentiment analysis could be used to identify several issues, such as:
- Clusters of disenchantment in teams, when linked to performance figures
- Reputational risk, such as rogue employees spreading rumours or sharing confidential information
- Pre-empting health and safety breaches, for example if incidences of people stating they do not feel safe increase
- Perceptions of leaders, perhaps after a change in senior management
- Flight risks, as in determining if high value employees may be about to leave
- Diversity and inclusion, for example whether certain groups dominate particular departments and the impact this has on others
- Detecting fraud and misconduct, or compliance breaches
Data could be drawn from multiple sources, including:
- Emails
- Internal social media or networking platforms such as Slack, Workplace by Facebook and Microsoft Teams
- Annual employee surveys and other questionnaires where there is a free-text option
- Chats on WhatsApp or other employee communication channels such as Skype and Zoom
- Exit interviews or feedback from candidates on the recruitment process
- External social media such as Twitter or websites such as Glassdoor