by David Pinto
Consumers have been doing sentiment analyses for quite a while. They look through unstructured customer feedback and figure out if it is positive or negative based on a raw score. Natural language processing tools use text from a variety of sources, such as emails, social media, and review sites, to break down what people are saying, put words in context, and see if they are positive or negative for a brand.
Has this been done before?
When the US Federal Energy Regulatory Authority looked into one of the biggest corporate failures in history -Enron- they analyzed 500,000 emails that were sent by employees that had been made available to the public. The signs were there, but had not been noticed at the time. Data analysis of these emails, which were sent years after the company went bankrupt in 2001, tells a good story.
After interactions between the company's top 150 executives were examined, index scores were evaluated at different points so when the company filed for bankruptcy suddenly it all made sense. 30 months before the company went bankrupt; tensions rose. This in-turn lead to an initial accounting fraud investigation and ultimately the demise of Enron. After further examination, it was found Enron was setting up partnerships to hide its losses. The records recorded by executives were explicitly different to the emails shared between each other. As a result, if there had been a way to track Enron's board of directors prior habits, the corporation may have have been able to change their toxic habits and avoid cultural suffering and a money meltdown.
How can it help my enterprise?
In the workplace, sentiment analysis could be used to find out about a lot of things, including:
Management perception: how people think about leaders and the respective business.
Identifying Frustrated teams: especially those who may be linked to performance figures.
Reputational risk: employees spreading rumors.
Preventing health and safety violations: if people say they don't feel safe.
Flight risks: employees highly anticipating to leave their jobs.
Diversity and inclusion: how people from certain groups work in certain departments and how this affects other employees.
Pinpointing misconduct: as well as policy violations.
How can this be done?
Management can make changes right away based on immediate feedback and address issues that would otherwise be unknown. This could be because a new policy has been put in place or because there has been a big shift in the benefits for employees.
All the signs of behavior can be found in language, which can help you find an employee problem before it gets worse & give you the chance to do something about it. Predictive analytics means you can see if the signs are there before an incident.
What about understanding context and sarcasm?
An academic group at a computational linguistics summit did a study of social media sentiment analysis. They found that these tools could remember that a tweet was sarcastic because an operator told them so, but they might not recognize sarcasm again because the context would be different. This also raises questions about how inclusive sentiment analysis is. If the team that makes the "rules" for the analysis isn't from a wide range of backgrounds, the algorithm may only look for things that are common to that group. It may not understand the subtleties of other cultures, generations, or people who are neurodiverse.
This should be of no concern in any corporate setting since the data is so vast and the use of sarcasm is seen by the system as normal error rate that needs to be constant.
As the story of Enron shows, however, its main selling point can be that it can pick up on changes in employees' feelings that they might not say outright. This gives it a level of analysis that other methods might not be able to tackle. With more businesses moving to long-term remote and distributed working, they can quickly find problems and fix them, which helps keep good employees and reduces overall satisfaction risk.
Okay, I’m in; what’s the Solution?
ELEFense analyzes company communications in real time & builds a unified dashboard to measure corporate culture, sentiment, and key words.
All the data is looked at the aggregate level, so no one can be identified and machine learning makes the interpretations more accurate over time.