Update Brand Sentiment Analysis Methods and Improve Accuracy with AI

improve sentiment analysis accuracy

If you’ve been analysing brand sentiment the same way for the past five years it’s time to make some changes. AI technology has advanced, and now there are tools to help you read the temperature of your audience with far greater accuracy and less effort than ever before.


We’ll explore how measuring and analysing brand sentiment has changed, and how you can improve accuracy with the help of AI.

A Brief History of Brand Sentiment

To get a feel for how measuring brand sentiment has changed over the years, most of us need only think of how our own online behaviour towards brands has changed.

Back when social media was pretty much synonymous with “Facebook”, people rarely interacted with brands online. Pretty soon, brands caught on to the benefits of having a social media presence, and soon social media became a ripe forum for customers to interact with brands, share their positive experiences and publicly air their grievances.

Back in the day, Yelp was the go-to place to share recommendations for local businesses. We hadn’t yet developed a culture of sharing reviews on brands, products and services. In 2012, Facebook was the go-to site for reading customer reviews, and shortly after, review sites had to tackle issues such as fake reviews and review gating. As of 2021, Google dominates the online review scene, and Google reviews impact your SEO score, but reviews are also dispersed across multiple sites such as Trust Pilot, Amazon, Gartner, Trip Advisor, etc.

It’s never been more important for brands to measure sentiment amongst their customers and address issues promptly to remain competitive in their market.

Back in the day social listening amounted to counting the number of likes, retweets, shares and shares. Back in 2012, the most advanced social listening tools had word clouds to show the most frequently used words with rudimentary and imprecise sentiment analysis. But AI technologies for text analysis have vastly improved, revolutionising what’s possible with social listening and brand sentiment analysis.

How Do You Measure A Comment?

Today, consumers generate content over multiple social media channels and review sites around the clock. The sheer volume of available qualitative data poses a new challenge to those wanting to get an accurate understanding of brand sentiment.


Comments and reviews are notoriously difficult to measure compared to structured data such as star ratings or the number of likes or shares. Human language is complex, there are multiple ways of expressing the same thing, and words that are negative in one context can be positive in another.


For example, the word “kill” within the context of pharmaceuticals is extremely negative, but within the online gaming community, the same word can be positive. The word “sick” within the restaurant industry is likely to be extremely negative, but amongst younger generations, it is a word synonymous with “amazing”.


But if we simply ignore the written text, we’re ignoring the overwhelming majority of information available to us. What do star ratings tell us about customer service, customers’ preferred product features or general experience of the brand or product?


Take this two-star Amazon review for Apple Airpods as an example:

“I love the way they link instantly and the sound is great, but apparently I must have small ear openings, because even after repeated wearings and trying all kinds of angles, they are not secure, need frequent adjustment and fall out of my ears with little provocation.”

This content tells us a lot about key product functionalities and product design. The device connects quickly and the sound is great, but the design doesn’t fit the customer. There’s a lot of useful information within this review that the star rating doesn’t convey.


There are hundreds of thousands of nuanced reviews just like this one. Until recently extracting all the information within this review would have required manual analysis. Now, advanced text analysis AI automates the process, giving you more detailed analysis on written reviews and comments than ever before.

Improved Brand Sentiment Analysis Accuracy with Symanto Technology

Symanto’s cutting edge natural language processing (NLP) technology understands human language like never before. Symanto’s state-of-the-art AI analytics turn customer feedback into actionable insights you can use to enhance your business strategies from marketing to product development.


Crawl thousands of online reviews, tweets and other social media comments within minutes. Symanto is connected to over 75 online portals including all major review sites.


Once the data is gathered, Symanto performs automatic analysis, identifying topics and subtopics within each review and arranging them into intuitive data visualisations. Each key word within a review or comment is assigned a positive, negative or neutral sentiment. Under each subtopic, you’ll find a word cloud based on accurate data to give you a clear understanding of exactly what your customers are talking about.


You can only fix a problem if you can see it.

Get Started With Symanto NLP Technology

Get clear and definitive answers regarding key consumer drivers and pain points from objective analysis of consumer conversations online. Get in touch or book your free personalised demonstration today.