Language is the fundamental currency for how people express themselves.
We use language in our personal and professional lives daily. In reality, we're telling an ongoing story that encapsulates our lives, but there's also value in the data for companies that need to understand what's going on under the surface. The process of mining this data for insights is called sentiment analysis.
The concept behind sentiment analysis is pretty simple: analyze large amounts of text and categorize the data by sentiment, or attitude. This analysis is done by blending natural language processing with machine learning. Sentiment analysis relies on open, unstructured text from conversations, emails, and other types of text-based qualitative inputs. Additionally, machine learning is used to train the system on words to look for that might signify issues.
Using Email Data for Sentiment Analysis
One example of a sentiment analysis platform is the type that analyzes email data to determine engagement, leadership, alignment, and more. It’s common for systems to look at engagement as a factor, but because these systems plug into the email server data, it can look more specifically at who is emailing whom, whether they are having personal or work-focused conversations, and more.
While the system is anonymous and does not identify users directly, it can derive sentiments down to the departmental level, giving employers the opportunity to address issues and hot spots before they become a major problem.
Extrapolating Intent from Comments (or "How to Understand What People Really Mean")
Other AI-based systems are similar in their approach. Several vendors are now developing a blend of analytical intelligence and emotional intelligence to get a fully accurate picture of what is going on in the organization. The analysis then powers predictions, custom recommendations, and more. Instead of focusing on email data, these systems pick up other qualitative inputs from performance review comments, social commentary on the company intranet, or in employee survey feedback.
These machine learning algorithms have been trained to the point where they can understand general sentiment by extrapolating the user’s mood from text. For example, if someone types, “The internet connection is too slow at the office,” the system intuitively knows this is a negative issue. Alternatively, if someone types, “I have a great team,” the system will associate this with positive feelings. While it is easy to look at these examples and understand the intent from a human perspective, it’s not as easy for machines to navigate the nuances of language, and that doesn’t even involve the effort and challenge required for performing this analysis tens of thousands of times.
The value of these analytical activities is clear: by understanding the general mood for a population of employees, whether globally, by department, or by location, a company can better serve those workers and meet their needs, driving engagement and the bottom line.