Learning how to analyze brand sentiment is important to find out exactly how customers feel about your company, products, or services. Finding out where your company falls – from high praise (positive) to total disapproval (negative) – can be time-consuming and tedious if you have to sift through large amounts of data manually.
However, with advances in AI technology, analyzing brand sentiment is now as easy as running text through tools that automatically “read” for positive, negative, and neutral sentiment. Sentiment analysis tools can automatically analyze text data using machine learning, to quickly and accurately classify user sentiment, without the need for dozens of employees.
In this post, learn how sentiment analysis can comb through brand mentions to detect emotions, like happiness, frustration, sadness, and more, and how it can benefit your business.
Brand sentiment refers to the underlying emotion expressed in a mention of your brand. Brand sentiment can convey positive, negative, or neutral opinions in customer's comments.
Instead of focusing solely on quantitative data (likes or number of comments), brand sentiment goes one step further, analyzing the context behind the interaction to gain deeper insights.
We all know the adage: The customer is always right. There is a wealth of data about your brand on the internet: news stories, social media comments, blog posts, forums, reviews, and so on.
A sudden uptick in mentions across the internet may seem positive, at first glance. However, it’s important to know the quality of that data – is sentiment positive or negative? Analyzing sentiment of your brand over time will keep your finger on the pulse of your customers’ thoughts and feelings, as they relate directly to your brand’s image and performance.
Let’s take a look at how monitoring brand sentiment over time can help:
Social media monitoring of your brand in real time can help you detect problems right away.
Take United Airlines, for example. A passenger incident led to a spike in negative social media mentions, after the company was accused of racial profiling. The topic spread like wildfire to China, where the episode became the number one trending topic on Weibo, a microblogging site with almost 500 million users.
And this all happened within mere hours of the incident. In situations like this, sentiment analysis can notify you of negative issues right away, so you can deal with them before they escalate into a bigger problem.
Sentiment analysis can help determine which brand mentions are most urgent. You’ll know when you need to reach out to customers, whether to thank them for praise or help correct an issue.
Imagine a social media influencer or well-known blogger has praised your brand – you’ll be able to join in the conversation to further improve on your brand image. Conversely, if there is a product issue or customer complaint, you can prioritize these issues first and respond within minutes.
Track your campaigns with analysis of reactions on Twitter, Facebook, and beyond. You might want to tune into a specific point in time to look at press mentions on the day of a new campaign launch. And you can follow the analysis as it trends up or down.
Take Dove for example. The company posted an ad on Facebook showing a woman transforming into an entirely different race after using their product. They received a huge backlash from women saying that the ad made them angry and uncomfortable. Dove picked up on these negative sentiments right away, removed the ad from Facebook, and released an apology.
Quick actions like this, using sentiment analysis tools, are essential to limit damage to your reputation and retain clients. And can quickly tell you if a campaign or ad is successful or not.
New design feature? Find out what customers are saying right after product launch. Or comb through years of reactions you may have never seen. You can search for specific keywords pertaining to a new product or feature to find only the information you need.
Companies like Instagram are constantly releasing new features – like their in-app video trimming tool. And they need to know the public’s reaction right away, or it could hurt their bottom line. With brand sentiment analysis, you can tap into exactly what you need, right after the new feature is launched. And you’ll know when to make changes if necessary.
With machine learning and targeted sentiment analysis you’ll know where your brand stands on a day-to-day basis. You can keep an eye on your public image, as it rises (or falls) over time. Machine learning analysis is far better than human analysis because machines don’t alter their criteria.
Your results will be consistently accurate, and you can follow them over time – without having to worry if you are making the right decisions or not.
There are plenty of tools out there that can help monitor the public sentiment of your brand online – cost-effectively, and in real time. Take a look at five of the top choices:
MonkeyLearn is a machine learning platform that offers an easy-to-use pre-trained sentiment analysis model that you can try out for free. MonkeyLearn’s text analysis tools can be integrated with apps (like Excel, Google Sheets, Zapier, and Zendesk), so you can simply dive in with software you already use.
And if you need a brand sentiment model tailored directly to your industry and your specific needs, you can train your own sentiment analysis model. It’s easy. No coding necessary!
Meltwater began in 2001 as an online media monitoring company to scour the news for trending information and individual client mentions. They now have massive news and social media databases that their AI software uses for media monitoring, social media listening, social media management, PR analytics, and influencer engagement tools.
Rapidminer uses data mining, text mining, and predictive analytics to break down unstructured data (online reviews and social media posts), along with structured data (publications and documents), to advance marketing, improve product development, and keep an eye on risk management. Users may enter raw data, like databases and text, which are analyzed on a large scale.
Hootsuite’s motto, “Build social intelligence into your brand strategy,” is accomplished through instant analysis and constant monitoring of millions of online conversations happening about your brand.
MeaningCloud offers a number of SDKs and plug-ins that increase usability and may be embedded in and optimized for a variety of applications. MeaningCloud allows users to merge any number of resources (dictionaries, taxonomies, sentiment models, etc.) to adjust for their personal requirements.
This simple MonkeyLearn tutorial will walk you through creating a personalized sentiment analysis model that you can use to monitor sentiment on social media.
Sign up to MonkeyLearn for free to instantly access pre-trained models, then follow this step-by-step guide to learn how to dive into creating and training your own model for your specific needs.
Create your sentiment analysis model:
Once you’ve signed up, go to the MonkeyLearn dashboard and click ‘Create a model’ then click ‘Classifier’:
To carry out sentiment analysis of your brand, you’ll need to choose Sentiment Analysis’:
Upload your social data gathered in CSV or Excel files and choose the column with the open-text data:
Choose the column you want to use:
Now you’ll need to train your model for brand sentiment analysis. In this example, see how each tag (Positive, Negative, or Neutral) is manually selected based on the polarity of opinion. Once you’ve tagged the first examples, the model will begin making predictions on its own. You can change them if the prediction is incorrect:
Once your model has been trained with a few examples, you can paste your own text to see how it’s classified:
MonkeyLearn shows stats to follow the performance of your brand sentiment classifier for accuracy, F1 score, precision, and recall. The keyword cloud gives a helpful visual reference to show the most used terms for each sentiment.
You may need to tag more data to improve accuracy of your model. In this example below, we need more data for the Negative category.
The more data your tag, the more properly trained your classifier becomes. You can improve accuracy by searching for false positives and false negatives and re-tagging them correctly. Here’s how:
Once trained, your model can begin to analyze thousands of tweets in seconds! MonkeyLearn offers a few different options to integrate your Twitter data with your new brand sentiment model:
Brand sentiment analysis is a valuable technique to find out exactly how the public feels about your brand at any given moment. There are a number of powerful machine learning tools you can implement to get the information you need.
Take advantage of all the useful opinions about your brand that are just waiting to be mined with sentiment analysis. MonkeyLearn has you covered with user-friendly tools.
Sign up to MonkeyLearn for free and get started right away.
March 27th, 2020