How to explain sentiment analysis to your gran

Sentiment analysis does what it says on the tin. That is, it analyses sentiment. But what exactly does that mean? Trying to explain sentiment analysis further than that might not be so easy.

If you’re familiar with sentiment analysis, it seems self-explanatory. But for some people, the term ‘sentiment analysis’ could mean anything from measuring the romance of card verses to mood detector machines.

Fortunately, sentiment analysis is altogether more useful than any kind of pseudoscience contraption. (And more straightforward.) So, here’s everything you need to know to explain sentiment analysis.

What is sentiment analysis?

The best way to start to explain sentiment analysis is to first define exactly what it is. So, sentiment analysis is technology that identifies the sentiment behind written text.

That written text could be from emails, blog comments, online reviews. It could come from social media messages, or live chat conversations. Whatever the source or medium, sentiment analysis would run the message through its algorithm to calculate the mood it holds.

From there, it produces a sentiment score from 1-100. This score can be based on whichever metric you’re keen to measure: from customer satisfaction levels to sales probability.

In a nutshell, sentiment analysis is the process of taking text and analysing it for the mood it reveals.

Where does sentiment analysis operate?

Sentiment analysis runs on your computer within a software solution. You might find it as a standalone solution, or as a feature within a piece of communication or data processing technology.

It might run automatically, as part of an ongoing analysis process. You could also manually enter specific text you want analysing.

So, explain sentiment analysis as a language parsing tool that needs text in a digital format to function. (No physical card verses or mind reading possible.)

How does sentiment analysis work?

To explain sentiment analysis, it’s also helpful to understand how it works. So, sentiment analysis parses a piece of text in search of pre-defined key terms. These are terms that it understands as positive, neutral, or negative.

In other words, sentiment analysis knows that words like ‘great’, ‘happy’, and ‘friendly’ are positive. So, when it finds them, it assigns that text a positive sentiment.

The software also knows that ‘pointless’, ‘awful’, and ‘annoying’ carry a negative connotation. So, these words earn the text a negative sentiment.

Who can use sentiment analysis?

You can train sentiment analysis to your specific industry. No two organisations operate in the same way. So, sentiment analysis tools offer flexibility on keywords.

Sentiment analysis technology will typically come to you already pre-fed with a database of common keywords. This means it can recognise obvious instances of positive and negative feedback, right from deployment.

You can also teach the technology to recognise words that pertain to your offering. For example, the word “downtime” might be flagged as a negative feedback term for a software company. But, for a hotel company, it could prove neutral or even positive.

So, any team and industry can use sentiment analysis. You can feed it with training data relevant to your field, and it will become an expert in that area as well as a great mood generalist.

When is sentiment analysis used?

With sentiment analysis all incoming messages, comments, reviews, emails, and text messages can be analysed and given a sentiment score. When you explain sentiment analysis, then, covering the opportunities to use the technology is a must.

In customer service, live chat sentiment analysis gives support agents a real-time feed of how customers are feeling. Meanwhile, requests, emails and support tickets can be assigned a sentiment score to give a sense of urgency and priority to each issue.

Elsewhere, sentiment analysis can help track social media mentions. It can flag up external reviews of your business or content. It can also give insight into the reception of your marketing efforts.

Why is sentiment analysis useful?

To conclude when you explain sentiment analysis, it’s worth mentioning why it’s so useful.

For example, the use of real-time sentiment analysis can help improve the quality, satisfaction and consistency of the customer experience. Gaining a real-time view of how customers are feeling allows support agents to adapt their responses accordingly. The result is a consistent human touch with empathetic, emotionally intelligent responses.

Sentiment analysis also gives you a chance to put out small fires before they become big problems. Whether it’s in a support session, over social media, or within a review, sentiment analysis flags up negative sentiment about your brand. With this, businesses gain the opportunity to address concerns and complaints. (All before they are blown out of proportion.)

Sentiment analysis give businesses insight into the overall perception of their brand. They can track how satisfied their customers are. They can identify opportunities for upselling and case studies. When businesses follow up their sentiment analysis results with action, they stand to gain a lot.

Share the understanding

This article only scratches the surface of what sentiment analysis is capable of. And, as the technology continues to evolve, more uses may just come to light.

But for now, you know how to explain sentiment analysis. So, whether they’re technically challenged or just haven’t looked it up themselves yet, you can share the understanding with anyone.