Improve Sentiment Analysis Accuracy with AI

improve sentiment analysis accuracy

There are now highly advanced ways to find out exactly how customers feel about a brand or product. Find out how you can easily employ advanced AI and NLP technology to improve sentiment analysis accuracy and discover how to make the most out of publicly available consumer data.

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.

Remember Yelp? Back in the day, it was the preferred 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. Now 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 comments. 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 text analysis technologies 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.

3 Ways to ImproveSentiment Analysis Accuracy with Symanto Technology

Natural language processing (NLP) is nothing new, but the accuracy of these technologies has advanced in great strides over recent years.

The hype around ChatGPT at the beginning of this year has drawn public attention to the complexity and advanced capabilities of technologies that can understand human language.

Symanto is at the cutting edge of NLP research and its practical applications. For years, Symanto has worked to apply these technologies to help businesses quickly and easily process thousands of consumer opinions into useful, actionable insights within minutes. Not only does this technology improve the accuracy of sentiment analysis, it also provides deep emotional and psychographic information to help you better understand what drives consumer behaviour.

Companies have used Symanto for years to help enhance their business strategies, from marketing to product development.

Here are three ways Symanto helps companies make the most of consumer feedback:

1. Sentiment by topic

The most useful consumer reviews are often neither wholly positive, nor wholly negative. Take the example headphone reviews above. The reviewer gives a nuanced opinion on the functionality and the design of the product.

Most sentiment analysis tools would simply compare the number of positive words or phrases in the review (“love”, “great”) against the negative ones (“not secure”, “fall out”) and, in this instance, would simply mark this review as a “neutral”.

However, Symanto measures sentiment by topic, also known as “aspect-based sentiment analysis”. This means that the sentiment regarding the product’s functionality can be marked as “positive”, while that of its design can be measured as “negative”.

Through this method, businesses gain valuable insight into how customers feel about different aspects of a product or service. This helps improve sentiment analysis accuracy and guide better decision-making.

2. Emotion text analysis

Typically, when a company conducts sentiment analysis they want to know how the customer feels about their product, service or brand. Sentiment is measured as either positive, negative, or neutral, but falls short of describing the emotions a customer is feeling.

Based on Paul Ekman‘s Universal Emotions model, Symanto’s emotion text analysis detects seven emotions from texts: anger, contempt, disgust, enjoyment, fear, sadness and surprise.

Emotion text analysis enables you to expand your understanding of customers’ experiences. This can be particularly useful in prioritising upset customers for customer support and identifying opportunities for improvement in product or service quality.

3. Granular insights

Symanto uses interactive data visualisations to enable you to manually explore your data and get to the root of customer sentiment.

Our dashboards allow you to easily analyse the context of customer feedback and uncover hidden trends, correlations or relationships between topics, sentiments and opinions expressed by customers.

This level of transparency enables you to pinpoint key issues, improving sentiment analysis accuracy and ultimately helping you improve customer experience.

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.