AI – Sentiment Analysis

sentiment analysis

Want to know the secret to customer satisfaction and maintaining a great reputation online?… It all starts with Sentiment Analysis.

Usually, when people see the words Artificial Intelligence, Machine Learning, and Sentiment Analysis, one of two things happens: their eyes glaze over because they’re not “technology-minded” so this is simply ‘not relevant’, or they turn away as it’s too complex and no one has the time to sit and understand AI – let alone use it!

But, trust us when we tell you, using AI for Sentiment Analysis could be exactly what you need to increase business efficiency, choose the right prospect in an interview, and monitor brand awareness or customer satisfaction.

What is Sentiment Analysis?

Sentiment Analysis combines Machine Learning, Natural Language Processing, and Artificial Intelligence in an algorithm which analyses a piece of text to determine whether the sentiment behind it is positive, negative, or neutral.

Why would I need it?

There are a variety of important business applications for Sentiment Analysis algorithms. In this day-and-age where the average person spends 142 minutes a day on social media, emotions expressed via statuses, comments, tweets, and stories are important commodities from a business perspective.

It’s important you have technology which can monitor customer responses to your product or services in real-time – so you can respond and get involved while the conversation is still relevant.

social media on phone

Still confused?

KFC is a prime example of a business using Sentiment Analysis to keep up with social media trends and appeal to its younger consumers. Monitoring what the internet is saying about their brand helps KFC to track, build, and enhance its social media and marketing campaigns to target what is important to consumers.

How Does Sentiment Analysis Work?

The best systems for Sentiment Analysis combine expert systems, Machine Learning, and deep learning to explore consumers’ emotions.

The most common elements that a Sentiment Analysis algorithm will look out for are:

  • Keywords that communicate strong emotion – words such as ‘love’, ‘need’, ‘want’, ‘perfect’, or ‘hate’, ‘worst’, ‘don’t buy’, ‘awful’ etc.
  • Emojis – algorithms can also be programmed to understand emoticons which often offer the emotional “punctuation” of social posts. If your users are posting angry faces, for example, this is something you may need to address.
  • Sarcasm/slang –  We’ve all seen sarcastic comments and reviews scattered across social media. But, because they incorporate positive words used ironically (e.g. ‘the best company to go to if you want a printer that doesn’t even work’), it could be easy for Sentiment Analysis algorithms to see this review as a positive comment, right? Wrong. These clever data analytics tools understand the power of sarcasm and are programmed to identify when words are being used to convey their opposite emotions.

It’s pretty impressive.

Why should you be interested?

Right now you’re probably thinking: ok, so Sentiment Analysis monitors reviews, but I can read customer ratings and satisfaction surveys, so why should I be interested in this kind of tech?

Well, Sentiment Analysis isn’t just used to monitor ratings. This kind of cutting-edge technology has extremely important uses that can impact the lives of entire countries.

For example, we’ve all heard of scandals where politicians manipulate voters’ emotions. With sentiment analysis, political parties can measure their influence through monitoring news articles, publications, or even the geographical areas that favour one party, and the changing attitudes over time.

One unforgettable case of Sentiment Analysis in politics was in the 2016 US Elections. While many polls concluded that Donald Trump was going to lose, experts had noted that people were generally disappointed with the current system under Mr Obama, and these claims were all backed with strong evidence from Sentiment Analysis that Mr Trump was actually gaining influence with voters. And of course, the technology was right!

polling votes

Another great use can be seen across large organisations worldwide when adhering to regulations and legal compliance. There are often so many legal documents and compliance regulations to refer to that it can be difficult for companies, large and small, to keep track.

Luckily, with Sentiment Analysis compliance monitoring can be easy – using tagging engines and watching your organisation over time, carefully coded algorithms can ensure business are always working within their compliance regulations.

We’re here to help

Whether you’re looking to analyse your customer satisfaction or an interview transcript to determine how your prospects truly feel – we’d be happy to discuss how we can use Sentiment Analysis and AI to create an innovative technology solution.

Posted on 17th September 2020 in AI, Technology

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