Glossary term

Sentiment Analysis

What is Sentiment Analysis?

Did you know that 89% of customers read reviews before purchasing products online?  

Customer opinions can make or break a brand—one negative review, and you risk losing a sale. A slew of negative chatter about your brand online, and you risk severe damage to your reputation. 

Enter: sentiment analysis, which determines the emotion behind pieces of writing. It uses text mining to analyze customer feedback, survey responses, and product reviews to determine if they reflect a positive, negative, or neutral attitude. 

In more technical terms, sentiment analysis combines text analysis, machine learning, natural language processing (NLP), and biometrics to break each sentence down into chunks and assign each chunk a weight sentiment score based on a pre-determined scale. These scores can tell you how positive or negative a piece of text is. 

Why is Sentiment Analysis Important? 

Goal #1 of any business: Retain customers and increase revenue. 

So, how do you do it? 

According to a recent Harvard Business School paper, every star increase in online reviews results in a 5% to 9% increase in profits—there’s an average 18% profit difference between companies with three- and five-star ratings. 

This just goes to show how important it is not only to provide customers with excellent products and services, but to encourage them to write positive reviews and share them online. 

If you take advantage of sentiment analysis, you can understand how customers feel about your brand, or specific products, at scale. If you had to comb through every online review or mention of your company, it would take hours and would likely make you want to rip your hair out. With sentiment analysis, you can sort through large volumes of data and analyze text in real-time, making it easier for you to act on what’s being said about your brand right now. It also removes human bias from the equation, ensuring that all text-based input is analyzed consistently to get more accurate insights. 

Sentiment Analysis Use Cases 

Sentiment analysis can be used across industries to improve marketing, customer success, and market research efforts.  

Restaurants, retailers, internet service providers, airlines, and more can use real-time sentiment analysis to quickly identify unhappy customers, categorize issues by urgency, and prioritize responses. It can also save brands from a potential PR crisis, allowing them to act before a user’s poor experience goes viral. 

Here are a few of our favorite real-world sentiment analysis use cases: 

Identifying Unfavorable Tweets at Lufthansa 

 Too often, the voice of the customer is represented anecdotally. Lufthansa wanted to change that, so they leveraged RapidMiner’s sentiment analysis capabilities to take their digital engagement strategy to the next level. Using sentiment analysis, they were able to identify unfavorable tweets and send specific call to action notifications to the affected department’s service team, who then reacted accordingly. 

Planning Product Improvements 

Sentiment analysis can do more than tell you if your audience thinks your products are “good” or “bad.” It can also use unstructured data to tell you how people feel about your product’s new appearance and if they’re making use of new features. If you’re looking to learn more about usability, for example, sentiment analysis can tell you what types of comments come up around the term “ease of use.” 

Determining the Best Communication Channels 

Marketing strategy in today’s environment usually involves quite a few communication channels—email, Facebook, Twitter, your website, text message, third-party retailers—the list goes on and on. However, have you ever thought about which channel customers respond most positively on? Sentiment analysis can help you figure that out, further improving your customers’ experience by optimizing how you talk to them across channels. 

Conducting Competitor Research 

Sentiment analysis can do more than just help you find out what’s being said about your business—it can identify the general attitude toward your competitors and their products, too. Using sentiment analysis, you can track what consumers are saying about your competition, see if and how they’re comparing your products, and uncover opportunities to improve your own company. 

Types of Sentiment Analysis Models 

There’s more than one type of sentiment analysis—depending on what exactly you’re trying to determine, there’s a specific model that will work best for your use case. We’ve laid them out here for you: 

Fine-Grained Sentiment Analysis 

This model breaks down the buzz surrounding your product according to how much people like it. It typically filters responses into polarity categories of very positive, positive, neutral, negative, or very negative, and can also be used on a scale of 1 to 10. 

Fine-grained sentiment analysis is often used to evaluate customer surveys and reviews or other short, straightforward texts. 

Aspect-Based Sentiment Analysis (ABSA) 

While the name might suggest otherwise, ABSA is actually more detailed than fine-tuned sentiment analysis. Aspect-based analysis can help you determine what part of your product, precisely, customers have a strong attitude toward. Thus, it helps businesses identify the features they need to prioritize and improve. 

For example, suppose customer feedback says, “The product is well-designed, but it’s overpriced.” Here, an ABSA model can be used to recognize the two aspects, design and price, along with their associated sentiment. 

Intent Analysis 

Imagine if you knew exactly what was going through your customer’s head when they wrote a review. While intent analysis isn’t mind reading, it can help you understand whether a customer has been doing market research and intends to make a purchase, or if they just stumbled upon your product. 

From there, marketing can use their time more wisely—sending targeted ads to customers who intend to buy soon, and not wasting budget on those who are unlikely to make a purchase. 

Emotion Detection 

Is someone so excited about your app that they want to shout it from the rooftops? Are they frustrated their new shoes didn’t arrive on time? Are they worried the food they’ve been feeding their dog is giving them an upset stomach? 

Using emotion detection, you can determine precisely which emotion is driving each of your customer’s reviews and respond accordingly. Since words can often be misinterpreted, many advanced emotion detection models also leverage machine learning, making them more robust, reliable, and accurate. 

Get to Know Your Customers with RapidMiner 

How you feel about a brand plays a big part in whether or not you’ll buy from them. Now more than ever, you don’t want to risk bad press, and with sentiment analysis, you can stay ahead of any potential knocks to your brand reputation. 

Luckily, you don’t need technical expertise in ML, or experience with Python or R, to perform sentiment analysis. With RapidMiner’s visual workflow designer, you can discover data-driven insights about your customers through text mining of online reviews, social media comments, and surveys—zero coding knowledge necessary. 

Ready to give it a try? Request a demo to see how you can start transforming your business today! 

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