Social media is a valuable tool in customer experience. According to “The State of Customer Experience” report from Genesys, 27% of consumers surveyed have used social media for a service interaction in the past 12 months. And about one-third say they’d mention a service interaction with a brand on social media when they’ve had a great experience. But not all social comments are going to be positive. This mix of customer emotions — positive, negative or neutral — toward your brand is called sentiment.

Sentiment analysis is an important metric that analyzes the online brand mentions and gives you insights into how your potential customers perceive your brand. However, to apply it correctly, you need to understand what sentiment analysis is, its benefits and how it works. 

In this article, we’ll cover those topics, and we’ll look at how some brands are using this metric for success.

What Is Sentiment Analysis? 

Sentiment analysis, also known as opinion mining, is the process that uses natural language processing (NLP) and machine learning (ML) techniques to analyze the online written pieces and determine the emotional intent behind them. 

Simply put, it involves understanding customers’ attitudes toward a particular topic, brand, product or service — positive, negative, or neutral.

For example: 

  • “I really like your new website design.” – Positive 
  • “I’m not sure if I like the new design of your website.” – Neutral 
  • “I don’t like the design of your new website.” – Negative 

Since customers give their feedback more openly than ever before, sentiment analysis is a powerful tool for monitoring and understanding their opinions and social media conversations. This way, brands learn what makes customers happy or angry so that they can tailor their products and services according to customers’ needs. 

There are four types of sentiment analysis: 

  • Fine-grained: Analyzing reviews and ratings and classifying them into positive, negative, and neutral. 
  • Aspect-based: Identifying the opinions on a specific aspect of a product or service. For example, TripAdvisor uses this approach to identify the sentiment behind the customer feedback and the service itself. 
  • Intent-based: Identifies the intention of the customer—whether they want to purchase something or just browse.
  • Emotion-based: Identifies the emotion of a customer behind the mention. This can include sadness, anger, happiness, satisfaction, frustration, etc.

How Brands Use Sentiment Analysis

Let’s look at some of the popular applications of sentiment analysis.  

Brand monitoring: Customers talk about your brands, products and services on the internet and share their recommendations. Blog posts, social media platforms, product reviews, news articles and discussion forums are excellent sources of business information. These are filled with customers’ opinions and comments that can help assess your brand health if analyzed. 

With social listening tools and performance dashboards, such as with Genesys Cloud Social, you can also check the ratio of positive and negative mentions within conversations. You can do this at the brand level, sub-brand level or even more granular as needed. This can also help you identify what aspects customers find helpful as well as where negative sentiments tend to collect. 

Preventing reputation issues: Reputation disasters can happen to any brand. And it’s nearly impossible to predict when your business might fall prey to a reputation crisis. 

But when the brand gets the crisis response wrong, it can go viral for all the wrong reasons. Analyzing online sentiments can be your best bet to avoid these crises. 

Sentiment analysis is your secret weapon when it comes to managing brand reputation crises. It helps you identify the spikes in negative mentions so you can spot a reputation crisis early and act quickly, leaving lesser chances of spreading it online.

Measuring campaign effectiveness: Similar to brand monitoring, you can track the mentions of your marketing campaigns and identify how users perceive your message by measuring the overall sentiment of their comments, shares, likes and posts. 

This is especially useful if you’re launching a new product or service. For example, if you’ve launched a new product and noticed a sudden spike in your customers’ sentiments, it might be because of something related to your new product or how you marketed it. You can also adjust your settings so that you can get even more granular. If your company is wondering how a specific feature for a new product is being received, you can track sentiment for that specific product and one of its features based on keywords.

So, track your marketing campaigns as they launch to find out your customers’ sentiments. This way, you can learn what’s resonating and what’s not with their needs and preferences and tailor your future campaigns accordingly. 

Performing competitor analysis: Don’t only analyze your own sentiments. To take your brand’s online presence to the next level, keep an eye on your competitors, too. 

Analyzing your competitors will help you know what aspects of their products receive several positive or negative reactions. Plus, you’ll be able to see what’s working for them and what’s not, so you can repeat their successes and avoid their failures. 

Competitor sentiment analysis can also serve as a benchmark when you analyze your customers’ sentiments behind your brand. For example, if 40% of your mentions are positive, 35% are negative and the rest are neutral, competitor analysis can help determine whether it’s a good thing or something that needs improvement. 

Improving customer experience: Finding out what your customers feel about your brand, products and services is essential for your business. This is where sentiment analysis comes in.

It helps you monitor and understand customers’ opinions and feedback about your products and campaigns the moment they’re launched. Further, it helps you identify positive mentions conveying what they liked about your product and negative mentions showing bad reviews and problems customers are facing.

If you catch these negative comments early, chances are you can act on relevant suggestions quickly for this specific customer and improve the experience for others too.

How Does Sentiment Analysis Work?

Now that you’re aware of what sentiment analysis is and how it is used by brands, let’s get into the mechanics of how it works. 

Sentiment analysis is a form of data science. Or, more specifically, it uses natural processing language (NLP) to identify and classify the pieces of text as positive, negative, or neutral. There are three main approaches to sentiment analysis: supervised machine learning, rule-based system or a combination of both. Let’s look at bit more closely at each.

Supervised machine learning (SML): As the name suggests, this model doesn’t rely on manually created rules but on ML techniques. In this case, you have to build labeled datasets (similar to the data you’d like to analyze) to help the machine learn how to classify text based on different emotional tones. 

Since the human evaluators already determine the sentiments of the given dataset, the system uses sentiment analysis from the training set to label the new test set. After providing enough relevant data, feed the machine new, unlabeled data to see if it marks it correctly. If not, keep adding more data to increase the analysis’s accuracy further. 

A SML model consists of the following classification algorithms: 

  • Naive Bayes
  • Linear regression
  • Support vector machines 
  • Deep learning 

The advantage of this approach is that it can create models trained for many different contexts and purposes. For example, the word “apple” refers to fruit but also a leading technology company. A machine learning algorithm can be trained to identify what “apple” means when the word is used in the sentence. It can then apply this experience to analyze similar cases. 

Rule-based techniques: Unlike supervised machine learning, the rule-based system uses a set of manually crafted rules to identify the text sentiments. These rules may include various NLP techniques, such as stemming, tokenization, parsing, part of speech tagging and lexicons (i.e., dictionaries of pre-labeled words and expressions). 

Check out the following example: 

Word sentiment

  • Great 0.8
  • Good 0.5
  • Bad -0.5
  • Terrible -1

Here’s how the rule-based system works: 

  • Determine the polarity of a text by using sentiment analysis tools to break it down into individual components, i.e., words and phrases. Then, assign each component a score using lexicons. 
  • Generally, positive words such as good, great, helpful, and useful are assigned a positive score (above 0), whereas words and phrases like terrible, bad, and disappointing would get scored negatively (below 0). Plus, neutral words and phrases are assigned a score of zero. 
  • Next, add up the score for every word and divide the result by the number of words in the text. For example: The ambiance was good, but the food was bad. In this case, the score would be:
    (0.5 + (-0.5)) / 2 = -0 / 2 = 0
    Therefore, this text will be labeled as neutral by the system. 

You can also count the number of positive and negative words mentioned in the given text. If the number of positive words is more than the number of negative words, it indicates a positive sentiment and vice versa. Also, if both the numbers are equal, it means a neutral sentiment. 

Overall, the rule-based technique is much easier to deploy than machine learning. However, it can take a lot of time and effort to maintain. You have to keep adding new rules, which might also affect the previous results.

Hybrid approach: Hybrid systems combine the elements of machine learning and rule-based approach into one system to improve the accuracy of sentiment analysis. Data scientists often use these techniques alongside each other.

A rule-based model will try to classify the sentiment of the text. If the sentiment is not easily identified (when no or very few words from the sentence are available in the lexicon), a machine learning system will be used to gain better insights.

Is Sentiment Analysis Really Accurate? 

As sentiments are often subjective, it’s hard to expect a sentiment analysis tool to be 100% accurate. So, let’s explore the major challenges that affect the accuracy of a sentiment analysis model. 

Irony and sarcasm

In many cases, people often use negative words to express positive sentiments. This can be difficult for the machines to detect without knowing the context of the situation. 

Consider these two situations: 

  • The order is only 30 minutes late again. What a great start to the day! 
  • The order is only 5 minutes late again today. What a great start to the day!

Obviously, an order that’s 30 minutes late won’t make someone’s day better. But this can be tricky for machines to understand.  

Negations

Negations are a way of reversing the meaning of a word, phrase or sentence. For example, “I wouldn’t say the hotel stay was particularly good.” For accurate sentiment analysis, it’s crucial to identify the negation and detect which word or phrase is affected by it. 

Multipolarity 

Often, a sentence will express different emotions at once — it might speak positively to one topic or object but negatively to another. For example, “The pizza place on 2nd Avenue is much better than the pizza place on 4th Street.  This sentence says positively about the pizza shop on 2nd Avenue and negatively about the one on 4th Street.

A sentiment analysis system will consider the sentiment to be positive, considering the “much better” phrase. However, if you’re analyzing the mentions for the pizza shop on 4th Street, you might disagree. Therefore, you’ll need more sophisticated programming to score this sentence accurately.

Classification of Sentiments

 Sentiments can be classified into three main categories:  

1. Negative sentiments brands need to watch out for

Negative sentiments include brand mentions that have negative words/sentences attached to them. Many people believe that the presence of negative sentiments means that the campaign, product, or service is a failure. 

However, that’s not always the case. Such sentiments can also indicate opportunities for business. For example, they could mean that something is off and you need to find a solution to fix it.

Examples include:

  • Sadness: This happens when a customer is not satisfied with the purchase or if you haven’t replied to their request. For example, “Your response time is really slow. I’ve been waiting over two weeks.”
  • Anger: When a customer talks about how bad your product or service is. For example, “Your support team is useless.” 
  • Comparison: When a customer compares your product or service with that of the competitor’s. 

2. Positive sentiments brands need to monitor 

Positive sentiments include brand mentions that have positive words/sentences attached to them. Customers have positive feelings about your brand and want to recommend your products or services to their loved ones. The more positive brand mentions, the better your brand is perceived in the customers’ eyes. 

Examples include: 

  • Happy: When a customer loves your product or service and shares their experience with their friends, saying how amazing it was. For example, “Great service at an affordable price. We’ll definitely try this again.”
  • Extremely satisfied: When a customer recommends your product or service to their friends and family. Such customers are more likely to become your brand advocates. For example, “I really like how easy this product is to use and how it successfully helps manage my team — clearly, one of the best team management tools I’ve used.”

3. Neutral sentiments brands can work on to turn these people into positives 

Neutral sentiment is neither positive nor negative. This means that such brand mentions don’t contain any words/phrases that can be classified as positive or negative. Instead, they just state some facts. 

Such sentiments offer an excellent opportunity for brands to reach out to customers and ask them for detailed feedback and how their experience was. 

Examples include: 

  • No comments: When a customer seems satisfied with your product or service but doesn’t express their feelings clearly. In simpler words, it happens when the customer writes neither good nor bad about your brand. For example, “Good job, but I expect a lot more in the future.” Or “The movie was incredibly unpredictable.”

Why You Need Sentiment Analysis

In times when customers come first, and they give their feedback more openly than ever before, creating products and campaigns that appeal to your audience and make you stand out among others is crucial. This is where sentiment analysis comes into play.

Sentiment analysis is a valuable metric for businesses as it provides real-time feedback from your customers. Plus, it enables you to uncover your audience’s emotions about your brand, which can make your product, service or campaigns more effective.

No matter what stage your business is at, sentiment analysis should be an essential strategy to take your brand’s online presence to another level. Read this guide to learn more about about how to integrate social listening into your customer experience strategy.