Last updated on January 7, 2024

Suz Morris
Content Marketing Writer @ Yotpo
April 26th, 2022 | 8 minutes read

Analyzing customer feedback at scale through customer sentiment analysis is crucial for eCommerce brands.

Table Of Contents

Human communication involves a multitude of emotions, ideas, opinions, and sentiments. 

Whether you’re writing a comment on a LinkedIn post or a text message to your friends, your words convey your opinions and attitudes about any number of topics. 

Imagine using a computer program to skim the texts between you and your friends. Instead of reading each one to find out what they want to tell you, it would give you a quick summary:

They want to hang out on the weekends more, memes are preferred over emojis, and everyone wants to try the newest restaurant that opened up in the neighborhood.

This is a simplified example of how brands can apply opinion mining and sentiment analysis to help them unlock quantifiable and measurable customer sentiment at scale. Essentially opinion mining and sentiment analysis can be applied to a wide range of practical applications, from understanding your friends’ group chat to assessing public sentiment toward a brand to conducting market research. 

Oftentimes, assessing public sentiment is done by looking at social media mentions. While social media mentions are important to understand, we’re entering a world in which sentiment is also expressed on a brand’s website — where consumers are spending more and more of their time shopping. 

Likewise, opinion mining is now standard practice for brands. With the right eCommerce marketing solutions, brands can transform social media sentiment and written customer reviews — sometimes paragraphs in length — into measurable consumer sentiment with sentiment analysis.

What is sentiment analysis?

Sentiment analysis falls under the larger process of opinion mining. Opinion mining uses a combination of data processing and data analysis techniques to conduct Natural Language Processing (NLP). NLP runs computer programs that use artificial intelligence (AI) and machine learning (ML) to help interpret written language as spoken language.  

Ultimately, opinion mining identifies a range of opinions about various topics in given pools of text, conducted by natural language processing. These opinions are scored along a scale of positive to negative using sentiment analysis, resulting in consumer data reports that break down customer sentiment on a more granular level — quickly. Applying this process to customer reviews can make it easier for businesses to determine trends in attitude and mood regarding any number of topics related to their business.

Sentiment analysis and customer reviews 

Their efficiency in extracting sentiment trends from massive amounts of text, opinion mining, and sentiment analysis creates a unique opportunity to analyze customer reviews at scale. Today’s technology allows for sentiment classification of reviews beyond just analyzing star ratings. Essentially, with opinion mining, brands can understand a customer’s sentiment by processing the actual text within a review. 

Unlike surveys, which tend to influence responses with targeted and potentially biased questions, reviews provide businesses with a centralized source of organic reactions that authentically reflect customer opinions. Open replies and text boxes empower shoppers to describe things in their own words, enabling them to raise unexpected issues that brands may not have considered. 

For example, a beauty retailer that uses opinion mining to analyze its reviews could quickly learn that their best-selling eye shadow has a trend of negative sentiment around the topic of scent. They could also dig further to discover the specific problem, like a scent that is “too strong” or “too sweet.”

using sentiment analysis to analyze reviews (bad scent example)

With the help of data derived from opinion mining and sentiment analysis, retailers can easily find out what their customers love or dislike about their products and overall shopping experience, even if they’re receiving several thousand reviews each month.

How opinion mining and sentiment analysis work

Sentiment analysis and customer reviews are such a natural pair, meaning customer sentiment can be easily derived from customer reviews. And since the impact of customer sentiment is a strong indicator of satisfied customers and brand growth, it was only a matter of time before Yotpo’s Data Science team researched consumer trends in shoppers’ online reviews. 

The team used NLP to extract topics from the reviews, which leveraged deep learning technology — a subcategory of machine learning and AI — to train its own sentiment analysis model on the opinions expressed. You can take a look at more specific findings they uncovered within the fashion industry here.

Moreover, our Data Science team identified 1 million topics and 75 million related opinions in our review database alone.

Just defining an “opinion” required several iterations.

Yotpo’s Data Science team also trained the technology on more than 30 million reviews to home in on its ability to accurately identify opinions and topics and to group them by the similarity of meaning. For example, the words “shipping,” “shipment,” and “delivery” would form a single topic. This allows more opinions to be tallied and more statistically significant samples per topic.

The team then used sentiment analysis processes to score each topic and opinion on a scale of -100 (the most negative) to +100 (the most positive).

opinion mining and sentiment analysis

Sentiment analysis is designed to distinguish between conflicting sentiments about different topics within the same review. For example: “Great product, but slow shipping.”

Thanks to painstakingly crafted rules embedded within the programming, it also can sort through complex and contradictory human styles of writing — most notably,  sarcasm.

For example, it can tell that this sentence expresses negative sentiment:

“Top-notch delivery service — still waiting on my package four months later.”

And that this one is positive in tone:

“Top-notch delivery service — got my package yesterday!”

Extracting topics and sentiment from reviews

Data and deep learning aside, the team’s impressive findings were the sheer speed and accuracy (92%) with which their algorithms could identify trends in sentiment extracted from customer reviews.

As any busy business owner knows, there are about a million things to do before you can even dream of sifting through customer reviews. Concerns over fulfillment, personnel, product development, suppliers, budgeting, and more, make it near impossible to find the time.

After going to our Data Science team to assess the model they built, the team realized they needed to evaluate the accuracy of our model. To do this, the team asked our professional services (manual moderation) team to take a group of reviews and start manually extracting opinions and topics.

“They analyzed a few hundred reviews over three or four days before they came to us and said there was literally no way they could do it anymore… and that’s even without tracking trends.”

However, when the Data Science team gave our professional services team their programming script, it took only a few hours to run a sentiment analysis on all the reviews.

Ultimately, the Yotpo Data Science team identified the positive impact NLP and opinion mining has on quantifying customer sentiment through analyzing written text in reviews. Now let’s take a look at how customer sentiment about a brand’s products and reviews as a whole influence a brand’s sentiment.

How customer sentiment impacts brand sentiment 

It’s no secret customers turn to reviews to help make decisions about purchasing products. Whether they’re filtering through reviews to find more information about fit, quality, sizing, shipping, etc., shoppers empowered to explore and learn more about products via reviews have a higher conversion rate — nearly 53% higher

Taking this one step further, the same concept can be applied to leveraging reviews to understand brand sentiment. With the help of customer sentiment analysis, businesses can improve brand sentiment through the following strategies: 

Sentiment helps brands better understand their customers

Customer reviews are directly tied to your product catalog. They often include valuable feedback on customer service and come from verified customers who have first-hand experience with your brand. They are, in other words, the perfect place to look for a huge range of customer-initiated reactions and feelings about your products and business as a whole.

But without the tools to comb through them for trends at scale, it’s easy to miss important feedback from your customers. While relying on star ratings may seem like a quick solution to analyzing mounds of reviews, it won’t give you the whole picture.

Reviews aren’t black and white. A five-star review can contain important requests for an improved delivery time, while a one-star review can be mistakenly written off as “negative,” but may contain plenty of helpful details that can entice customers to buy.

A customer’s experience is rarely wholly positive or entirely negative, so while star ratings give you an idea of customer satisfaction at a glance, brands would be remiss not to dig deeper with the help of customer sentiment analysis.