How To Master Brand Sentiment Analysis – Beginner’s Edition

What people think and feel about your company, products, and services is your brand sentiment. 

In the absence of magical powers that can help you see into customers’ minds, brand sentiment allows you to dig deep into the mentions and messages that your brand receives to discover the underlying emotions that are being expressed.

Evaluating brand sentiment can enable you to respond to feedback effectively, pick up on customer pain points, track campaign reception and success, and diagnose potential issues before they spiral out of control.

But even if you’re just a small business or growing startup, you’re probably going to get hundreds of brand mentions monthly from review sites, blog posts, and social media. So how do you keep track of all this data and uncover what your customers are really saying?

Enter brand sentiment analysis! 

In this post, we’ll explore the importance of brand sentiment analysis, how it works, and the steps you can follow to analyze your public sentiment and improve business outcomes.

What is brand sentiment analysis?

As long as you’re targeting customers in today’s digital world, chances are people are talking about your brand online. Now you can’t just assume that everyone who mentions your brand only has nice things and you can’t know what is being said unless you see it for yourself.

Brand sentiment analysis is a technique that leverages artificial intelligence, machine learning, natural language processing, and named entity recognition to measure and interpret people’s thoughts, opinions, and feelings about your company. 

The goal of sentiment analysis is to mine available data from surveys, reviews, news stories, customer service exchanges, and social media posts/comments and determine whether the sentiment from these interactions is positive, negative, or neutral.

3 types of sentiment analysis

There are different varieties of sentiment analysis that you can put into action to gather brand insights. The right one for your needs will depend on how you want to make sense of customer feedback and messages and whether you want to focus on

• measuring polarity (positive, neutral, negative)
• pinpointing specific emotions (happy, angry, dissatisfied)
• ascertaining intention (interested, not interested)
• or the state of urgency (urgent, not urgent).

Here’s a look at the most common methods of sentiment analysis utilized by brands:

1. Document-based sentiment analysis

In document-based sentiment analysis, the sentence structure and composition, word representation, and document composition of a given text are analyzed to decipher the sentiment behind it. 

This type of sentiment analysis works best for simple sentences containing one sentiment, rather than complex sentences with multiple sentiments. Say a customer leaves the following feedback on a review site:

“I love Sycamore; their policies cover a lot of health conditions.

“There’s a lot to love about Sycamore. For starters, their premiums are affordable and they offer broad coverage even for many preexisting conditions. However, their customer service is ridiculous, you will have to wait for several minutes or hours to get a response.”

You can easily evaluate and categorize the first feedback as positive using document-based analysis because the text is concise and has one central emotion (love) and topic (policy coverage). 

However, since the second feedback conveys different feelings about your brand and discusses multiple issues, using document-based analysis may yield inaccurate results.

2. Topic-based sentiment analysis

When dealing with more complicated feedback like in the second example above, topic-based sentiment analysis can be relied upon to get the job done. 

This method uses keywords and recurring phrases to find and extract topics from a complex text and then evaluates the sentiments for each talking point one by one.

Consequently, you’ll have a more accurate analysis of the mood and content of the data you’re reviewing. 

3. Aspect-based sentiment analysis

Aspect-based sentiment analysis is an advanced technique that generates more precise results by drilling down into the sentiments surrounding specific features of your brand, product, or service using artificial intelligence. 

Consider this example: “Their designs are basic and the prices often feel a tad exorbitant but their outfits go up to size 20 which is nice and inclusive.”

When evaluating this statement, aspect-based analysis will go beyond providing a general sentiment or the sentiment for the topics “prices” and “outfits.” 

It will break down the different aspects discussed in the review and tell you that the customer sentiment for “design” was neutral, inclined towards negative for “price”, and resoundingly positive for “sizing.”

Case for brand sentiment analysis

Here are some of the ways you can put sentiment analysis to good use for your brand and the benefits you can expect to reap when you do:

Know your audience

Successful businesses know their customers like the back of their hands. One of the ways you can become better acquainted with your target market and discover their needs, wants, expectations of, and reservations about your brand and its products is through customer sentiment tracking.

Your analysis can reveal what your target customers value. The kind of services they’d like you to offer, what they love or hate about your brand, or why they chose your products over your competitors.

Ultimately, this knowledge will allow you to develop product, brand and marketing strategies that drive engagement, increase conversions, and address customer pain points.

Gather strategic insights

When Nike released their 2018 ad featuring Colin Kaepernick after he took a knee during the National anthem, their online mentions began overflowing with negative comments including #boycottNike hashtags. The brand’s net positive sentiment dropped from 23% to -4%.

To the average marketer, the smart thing for Nike to do would have been to pull the ad before it did serious damage to the brand’s reputation. 

However, using sentiment analysis, Nike discovered that the overall public sentiment for the campaign was positive and that brand mentions and sales were climbing higher.

Sentiment analysis empowers you to look beyond the surface and interpret feedback to unravel the true intent and polarity behind customers’ opinions. This way, you’ll have actionable insights to hinge future decisions on instead of relying on assumptions or the heat of the moment.

Pro-tip: If you’re delving in programmatic advertising, it’s a good idea to automate your entire brand campaigns and sentiment analysis through tools for better insights. 

Conduct campaign performance and competitive analyses

Analyzing sentiment can help you determine whether your campaign is well received by your target audience or on the way to becoming an epic fail. 

You can monitor changes in sentiment levels in real-time so you can swoop in and take action to fix the underlying problem if you notice negative mentions trending up.

You can also use sentiment analysis to spy on your competitors and see how you measure up against them or what they’re doing better that you can emulate. 

Knowing the negative sentiments around a competing brand will allow you to capture their customers by steering them towards your product and brand.

Get ahead of negative PR

The wonderful reputation you have spent years cultivating can crumble in the twinkling of an eye when negative reviews go unchecked and unfavourable sentiments are allowed to spread. 

Sentiment analysis tools can alert you whenever there is a sudden uptick in negative mentions so you can implement a plan to mitigate the issue before it becomes a full-blown crisis.

In 2014, Expedia Canada came under fire for its Christmas ad “Escape Winter: Fear” which featured a man who mistook the grating sound of his daughter’s novice handling of the violin for a terrifying snowstorm. 

Unfortunately for the brand, people found the out-of-pitch violin noises irritating and intolerable after viewing the ad a few times. Naturally, they took to social media in droves to air their frustration and chastise Expedia Canada. 

However, by monitoring brand mentions, the company was quick to react to the wave of negative sentiment by pulling the ad and replacing it with two new ads.

Boost customer service

Have you ever written a negative comment about a brand or product without tagging the company in question? Then a few minutes later you get a response from a customer rep asking you what the issue is and how they can help.

That’s sentiment analysis at work. It helps brands pick up negative mentions quickly so you can step in to remedy the situation before it completely sours your image in the customer’s mind.

Since sentiment analysis enables you to respond to comments and feedback swiftly, it shows customers that you’re not just paying lip service to them and their problems, which increases their trust and loyalty to your brand.

Focusing on the Voice of Customer (VoC)

Sentiment analysis is useful for zeroing in on what your customers are saying or not saying when they talk about your brand on social media, in surveys, or their conversations with customer support.

You can use customer experience surveys and NPS surveys to solicit feedback and find out the likelihood of customers recommending your brand to someone they know. Then analyze the data to understand why your results look the way they do.

Sentiment analysis creates room for you to join the conversation and make customers feel heard by ensuring that praise doesn’t go unappreciated and comments or questions don’t get ignored across channels and touchpoints.

Steps to perform a brand sentiment analysis 

Knowing the importance of sentiment analysis for brands is only half the battle. To unlock the value it offers, you need to put it into practice, but how do you go about analyzing your brand sentiment?

1. Explore social media platforms 

The quality of the data you collect will influence the accuracy of your analytic results. It’s better to focus on online platforms and social media management where your customers hang out and talk about you the most.

After deciding on the platforms you want to source insights from, it’s time to begin gathering the data. You should be able to use your brand sentiment analysis to collect the data you need from your chosen platforms using scraping technologies or live API integration.

Alternatively, you can upload the mined data into your sentiment analysis manually with a .csv file.

2. Assign context to the right mentions

The data you’ve collected won’t mean anything until you process, analyze, and extract the information lying dormant within it. Assuming you choose a good brand listening tool like Keyhole, it’ll do most of the heavy lifting in this arena.

However, it’s up to you to help the tool perform optimally by training it to recognize and classify your data to your specifications. You can set up custom tags for topics and aspects and train your analytic model to associate certain comments in a text with relevant aspects and themes for advanced-level insights.

This way you can rest easy knowing that the algorithms will properly interpret the context behind a customer’s feedback even when the language features slang and colloquialisms.

3. Leverage the right tools 

Unless you’re only getting a handful of brand mentions daily, you cannot effectively conduct sentiment analysis on your own, without technological assistance. Sifting through all that data can get boring and exhausting quickly, while also consuming huge chunks of your time that could be better spent on more valuable tasks.

Investing in a brand sentiment analysis tool allows you to automate the process, listen to public feedback regarding your brand/product, keep an eye on your competitors, and stay up-to-date on the latest happenings in your industry. 

What’s more, relying on technology, minimizes human errors and biases, so you’ll end up with sentiment scores that factually represent how people feel about you.

4. Talk to key players in your company

Once you’ve collated and evaluated your data to uncover fresh knowledge about your customers, brand reputation, product positioning, and more, the final rung on the ladder is reporting your findings. 

The insights you’ve gleaned will help management and other departments like sales, product, and customer service understand where to focus their efforts to increase customer satisfaction, brand awareness, and engagement. 

Make sure you present your results in an accessible format. Everyone should be able to look at the data and understand what it’s saying and the actions they need to take to fix any sticking points that are contributing to customer churn.

Roadblocks in brand sentiment analysis

Despite the impressive strides that have been made to improve the accuracy of sentiment analysis, there are still some challenges that can impact your machine learning model’s ability to classify and interpret data in a precise manner.

Sarcasm in mentions

Sometimes, people use backhanded compliments to express negative feelings. This can make it difficult for sentiment analysis tools to correctly decode what is being said and they can end up categorizing ironic or sarcastic feedback as positive when the sentiment, in reality, is negative. 

Lack of context

It’s easy for sentiment tracking tools to analyze subjective texts and accurately determine their polarity because the sentiments being communicated are clear as day. But things get trickier when working with objective texts where the sentiment is not so obvious. 

This is because the algorithm may not have enough contextual information to accurately determine whether the polarity of a given text is negative or positive.

Let’s say you get these responses to a survey:

“That’s highly unlikely.”

“Not much.”

Out of context, these comments will probably register as negative. But what if this is the question they were responding to?

“Would you consider switching to a different service provider, and why?”

“What would you change about our user experience?”

This context sheds new light on the meaning behind the responses and swings the sentiment analysis results in the positive direction.

Improper attributions

Machine learning models are trained on the information you feed them. If there are inaccuracies, biases, or other irregularities in the training dataset, it can cause the sentiment tracker to mislabel sentiments in a text. This can lead to flawed outcomes. 

Build a positive brand reputation with Keyhole

Sentiment analysis is a crucial tool for businesses that want to connect with their customers on a deeper level, scale brand awareness, and maximize the ROI of their marketing efforts. 

To get actionable insights and understand how people experience your brand, you must select software that offers advanced machine-learning models with remarkable accuracy. 

With Keyhole’s in-depth brand sentiment analysis tool you can monitor your online mentions, measure campaign performance, and benchmark your results against that of your competitors in a matter of minutes. 

Ready to start tracking your brand mentions and analyzing the sentiments behind them? Start your free trial with Keyhole today.

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