🧐 What is sentiment analysis?

Simply put, sentiment analysis identifies emotional tone and opinions within text. It’s an important part of natural language processing (NLP) that works on multiple levels to extract insights from documents, paragraphs, and sentences.

These insights help businesses problem solve internally, and for their customers.

Sentiment analysis is a big part of social media monitoring, brand monitoring, customer feedback, workplace analytics, product analysis, and market research. In fact, Google, IBM Watson, and Microsoft have used sentiment analysis to transform businesses for over a decade.

But it’s not about language alone. The “smarts” in NLP are about combining language with context as different words will have different impacts in different environments. However, by combining manually-crafted rules with machine learning techniques - and constantly updating, the margin for error in sentiment analysis continues to grow smaller and smaller.

In the workplace, sentiment analysis is particularly useful for gauging how your team is feeling at different times depending on their activities, participation, and days of the week.

There are certain trigger points, such as low sentiment, that indicate your team may need extra support. There are also opportunities to pinpoint situations and activities to boost wellness and productivity.

High sentiment - feeling good

Some language examples are:

• Happy, awesome, energised;

• This report is going really well;

• I reckon you’ll to be pleased with the result.

Low sentiment - leading to or experiencing stress

Some language examples are:

• Frustrated, agitated, angry;

• This report just never ends;

• It’s all his fault;

• I’m at the end of my tether here.

Because Indie sits quietly in the background like a spell checker, it can give you patterns and trends to help you and your team determine when and how to provide extra support, to create a happier, more productive workplace. That is why its she helps you be so successful.