Sentiment analysis graph showing positive, negative, and neutral feelings

What Is Sentiment Analysis Marketing? Tips, Tools, and Techniques

Do you really know how your customers feel about your brand? And how can you use that insight to improve your marketing campaigns?

The answer: sentiment analysis marketing.

In this guide, we’ll explain how to use sentiment analysis in marketing. And how it can benefit your business.

Sentiment Analysis Definition

Sentiment analysis, or opinion mining, is the process of analyzing text to determine whether the tone is positive, negative, or neutral. It helps you understand how your customers and prospects feel about your brand. Based on what they say online. 

This can help you deliver better customer experiences. And develop stronger marketing strategies.

Sentiment analysis tools examine customer reviews, comments, and social media posts. Many tools express sentiment scores as a percentage. For example: 90% positive, 10% negative.

Sentiment analysis can help you understand and label feelings like:

  • Positive: “This is my favorite brand!”
  • Negative: “I had a terrible experience here.”
  • Neutral: “My meal was okay. Not sure if I’ll go back.”
Sentiment analysis graph showing positive, negative, and neutral feelings

But not every customer review reflects an overtly positive or negative experience.

Many tools go beyond detecting obvious words like “great” or “awful.” They use artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to assess how people truly feel. 

And uncover trends and insights to help inform business decisions. And marketing strategies.

For example, you may discover customer sentiment improved during a particular quarter. Or sentiment declined when you increased prices.

How Sentiment Analysis Works in Marketing

As a marketer, sentiment analysis provides a clearer view of how your customers feel. 

Here’s how to get the most out of sentiment analysis in marketing. 

Brand Monitoring

When you monitor mentions of your company online, you assess how happy customers are with your brand, products, or services. Since this type of analysis can help you find and address negative sentiment, it allows you to manage your reputation, too.

Sentiment analysis eliminates the need to read each comment manually. This can be time-consuming for businesses with a high volume of feedback across multiple marketing channels.

Semrush’s Media Monitoring tool automatically tracks brand mentions online. It also analyzes sentiment to help you understand how current and potential customers feel about your brand.

A graph showing brand mentions online in Media Monitoring tool

Campaign Insights

Analyzing comments on campaign assets like social ads allows for deeper insight into customer attitudes. After all, marketing and advertising performance is about more than clicks. And conversions.

This analysis can help you understand if and how customer sentiment is affecting campaign performance. Using this data, you can make an informed decision about how to respond. Or adjust.

Here’s an example:

Suppose you’re running a Facebook ad campaign that’s getting fewer clicks and conversions than usual. After reviewing the ad comments, you discover they reflect a negative sentiment. 

You update the messaging based on what you’ve learned. And sentiment and campaign results improve. 

A Facebook ad with negative user comments

Social Listening

Also known as social media monitoring, social listening lets you find and respond to conversations using keywords. Or hashtag alerts. It can reveal what customers say about your brand. When they think you aren’t paying attention.

Here’s an example:

Suppose you use social listening tools to search for keywords or hashtags related to your brand on Instagram. Apply sentiment analysis to find posts or comments. With a positive sentiment.

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Then, engage with the content. By leaving a comment. Or sending a direct message (DM). Follow the original poster. And continue to engage with their content over time. This way, you build a relationship. And potentially attract a loyal customer.

Market Research

Customer feedback channels like surveys and product reviews can generate a lot of data. From many different sources. With sentiment analysis, you can process data efficiently. And gain actionable insights.

For example, you could learn:

  • If customers liked or disliked your last product launch. Which can help you refine the next one.
  • What changes customers would prefer. Which can help you adjust your product or service.
  • The biggest pain points around your brand. So you can improve customer experience.

Influencer Discovery

Sentiment analysis marketing can introduce you to your brand’s biggest and most vocal supporters. It can also point you to the channels where they spend time. Which may help you find even more fans.

For example, you may find customers are saying positive things about your brand on YouTube. Upon further analysis, you realize the videos have helped shape sentiment about your company.

And led to more customers. And more positive videos.

Like this video on YouTube of a Semrush tutorial. It was created by a YouTuber to show their followers how to use Semrush. But it may also positively influence sentiment about Semrush. And drive leads. 

Semrush tutorial for beginners video on YouTube

Build relationships with fans to cultivate a community of social media influencers. Or ambassadors. As your fans grow, the positive consumer sentiment around your brand could increase.

Competitor Research

Sentiment analysis works just as well for assessing the competition. 

  • Assess your top competitors’ brand sentiment across online forums. And social media.
  • Analyze topics with the most negative sentiment to find gaps. Identify competitor products or services that regularly receive negative feedback. Then, create marketing campaigns that speak to customer complaints. While promoting your own brand.
  • Identify competitor campaigns with the most positive sentiment. And use them for inspiration.

Techniques for Sentiment Analysis

Sentiment analysis tools typically use one of three main techniques or sentiment analysis algorithms. Here’s a brief overview of each:

Rule-Based Systems

A rule-based system uses a manual process to apply rules to specific sets of keywords. Then, it tallies the number of positive and negative instances. To determine the overall sentiment.

Here’s how a rule-based system could look in practice:

It analyzes reviews, comments, and other inputs for:

  • Positive words like “great” or “love”
  • Negative words like “dislike” or “bad”

In a group of 100 customer reviews, suppose it finds 60 positive keywords. And 40 negative keywords. The sentiment is 60% positive and 40% negative.

Rule-based sentiment analysis bar graph with 60% positive sentiment and 40% negative sentiment.

This type of system is relatively simple to set up. But because it doesn’t factor in context, it may be less accurate. 

For example, rule-based systems may not process sarcasm. Or jokes. Or apply the correct label to a comment with positive and negative keywords.

Automated Processes

An automated process uses machine learning to assess sentiment. It trains on a wide range of inputs. And it continues to learn. And improve over time.

Essentially, it analyzes inputs like customer reviews. And uses ML to classify each one as positive, negative, or neutral.

This type of automatic system is more complex and time-consuming to set up. But because it can read context, it can provide more value to marketers.

Words like impressed, positive, pleased, indifferent, disappointed arranged in a cloud.

Hybrid Methods

Some marketers use a combination of the two, known as a hybrid method. By combining a basic rule-based and machine-learning approach, this method tends to provide the most accurate analysis. 

As a result, tools that use hybrid options are often the most valuable options for marketers.

Benefits of Sentiment Analysis for Marketing

Is sentiment analysis worth the investment? To help you decide, let’s look at four advantages.

Enhanced Customer Understanding and Personalization

Sentiment analysis gives you a deeper understanding of customer preferences. To better understand your ideal customer and fine-tune your buyer persona.

Buyer persona categories are demographics, professional status, psychographics, pain points and challenges, influences and information sources, and purchasing process

But what really matters is what you do with the insights.

The more you know about your buyer, the better you can align marketing efforts with their preferences. Monitor campaign results to ensure your content and messaging resonate.

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Improved Products and Services

Sales figures may indicate your launch was a hit. But customer reviews may suggest the opposite.

Monitor sentiment in feedback and online reviews. To learn what customers think of your products. And services. And see how their opinions change over time.

Use these insights for product development. Or improving services in a way that customers love. As a result, you can target ، goals like customer loyalty. And lifetime value.

For example, upon analyzing reviews, you may learn that customers love one product’s specific feature. This insight may prompt you to run more campaigns that focus on this feature. 

It may also encourage you to prioritize improving or enhancing this feature. To boost loyalty.

Better Brand Reputation and Crisis Management

Sentiment analysis tools double as brand reputation management tools. They can reveal unusual changes in sentiment. Or spot trending topics. Or news articles that mention your brand. 

Use this data to respond to a reputation crisis before it escalates. In a crisis, response time matters more than you may think. Addressing the issue promptly may help stop a crisis in its tracks.

For example, sentiment analysis may alert you to a spike in negative comments related to a company announcement. You don’t have to let this feedback turn into a crisis.

Instead, have your team carry out a response plan. In other words, respond to individual comments. Or publish a follow-up announcement that addresses the comments. And acknowledges the mistake. 

Respond to negative review: improve response time, show courtesy, acknowledge mistakes, keep it public, offer refund or discount, reach out to third-party authors, improve service/product.

Data-Driven Decision-Making

As a marketer, data likely guides many of your decisions. But if your data-driven marketing focuses solely on behaviors like clicks and conversions, you may not be considering the full picture.

Sentiment analysis can strengthen your decision-making process. As you build out marketing strategies, factor in what customers say. Rather than just what they do.

Real-World Examples of Sentiment Analysis Marketing

How can marketers use sentiment analysis in practice? Let’s look at two real-life sentiment analysis examples.

British Airways Holidays

British Airways Holidays, a vacation package provider, used sentiment analysis to understand its vast database of customer reviews. 

Using a sentiment analysis tool with NLP, the company evaluated customer emotions and feelings. For a large volume of reviews.

British Airways Holidays Reviews

The company used this data to understand what prompted customers to become loyal advocates. Then, internal teams used it to improve customer experience.

The result? A proactive approach to customer complaints. And a more efficient way to address issues.


Divante (now Cloudflight Poland), an ecommerce software development company, used sentiment analysis to find and engage with potential customers.

Divante (now Cloudflight Poland) post on X sharing "Managed Cloud Innovations" report

First, the company searched social media platforms for relevant keywords. Then, it identified prospects. Who were actively considering software development.

Using this data, Divante launched an outreach campaign to respond to these prospects. And help boost brand awareness. And sales. 

How to Implement Sentiment Analysis in Your Marketing Strategy

Use this short guide to incorporate sentiment analysis into your team’s marketing strategy.

Collect Data

One option is to request data directly from customers via surveys. Or feedback forms.

Another is to review publicly available social media posts, press mentions, and discussion forum posts. When you aren’t sure which forums or platforms to analyze, use One2Target.

As an audience analytics tool, One2Target helps you understand who your customers are. And where they spend time. It also reveals demographics, socioeconomics, and interests.

Open the tool from the “.Trends” drop-down menu in Semrush. Enter a domain. And click “Analyze.”

Click the “Behavior” tab. And scroll to the “Social Media” chart.

"Social Media" chart in One2Target tool shows which social media platforms the brand's audience visits most

Here, you can see which social media platforms the brand’s audience visits most. Now that you know your audience’s preferred platforms, you know where to focus your data analysis. And social listening.

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Below, we’ll look at how to analyze sentiment across platforms.

Conduct a Market Sentiment Analysis

Use your audience research to choose a market sentiment analysis tool. Choose one that can monitor the websites, forums, and social media sites where your target audience spends time.

The Media Monitoring tool tracks brands, products, keywords, and competitors. And uses NLP to process text. And analyze sentiment.

After installing the app, click “Get Started” to set up your search. Enter a brand, name, or keyword to monitor.

Let’s use “OpenAI” as an example.

"OpenAI" entered into the Media Monitoring tool

Type “OpenAI” in the “Monitor this brand” box. Then, add the social media profiles you want to analyze. 

Click “Create Query” to analyze online mentions.

Add social media profiles you want to analyze to Media Monitoring tool

Click the “Analytics” tab and scroll down to view the “Sentiment” chart. View a breakdown of positive, negative, and neutral sentiment. You can also see a graph of the sentiment over time.

"Sentiment" chart in the Media Monitoring tool

To see the data sources that power these graphs, click the “Mentions” tab. 

Here, you can see a complete list of all positive and negative mentions of the keyword, brand, or person you’re monitoring.

"Mentions" board for OpenAI shown in Media Monitoring tool

Use the “Filters” drop-down to sort by source, sentiment, and other factors. These filters can focus your analysis on customer experience on certain platforms. Or specific topics.

Apply Your Analysis 

Use these tips to incorporate findings into your strategy:

  • Set sentiment marketing goals: Identify your current customer sentiment scores (e.g., 75% positive and 25% negative). Decide how much to increase the positive sentiment. And when to complete it. Generally, 80% positive is considered good customer sentiment, according to LiveVox.
  • Choose an approach: Focus on the channels that will likely make the biggest impact. For example, design campaigns for channels where you have room to make the most sentiment gain.
  • Adjust and iterate your strategy: Measure sentiment regularly (e.g., weekly or monthly). And compare it to the previous period. Use the results to adjust your strategy. And continue making progress.

Sentiment Analysis Best Practices 

Choose the Right Sentiment Analysis Tools

There are countless sentiment analysis tools. From audience insights and customer analysis to media and brand monitoring platforms.

How do you know which will best meet your needs?

Consider these factors:

  • What type of data do you need to analyze? Do you plan to input surveys or feedback forms?
  • Which websites, blog posts, forums, news sites, or social media platforms do you need to monitor?
  • What type of entity do you need to analyze: brand, product, keyword, or person?
  • Do you need to analyze competitors?

Take note of your needs. Then cross-check them against the feature list when choosing tools for sentiment analysis.

Apply Contextual Analysis for Enhanced Sentiment Interpretation

Human language can be complicated. One word may mean four completely different things. That’s why it’s important to choose a tool that can understand context.

Tools with NLP algorithms can understand how the human brain forms language patterns. They can apply contextual analysis. And produce more accurate results.

Ensure Ethical Practices and Privacy in Sentiment Analysis

AI and NLP are powerful tools. But in some cases, they can create biases that affect how they analyze language. These biases may misinterpret customer emotions. Or dismiss statements as irrelevant or neutral.

Using reliable machine learning models can help avoid these issues. So can regularly reviewing data manually. To confirm accurate sentiment. And a lack of ethical concerns.

Transform Insights into Action with Sentiment Analysis Marketing

To understand how customers really feel about your brand or product, use a sentiment analysis solution.

With the right solution, you can learn much more than what customers say. Including how to meet their needs, build deeper relationships, and ultimately improve loyalty. And lifetime value.

With our Media Monitoring and One2Target tools, you’ll uncover how customers feel about your brand, product, or service.

Start analyzing sentiment around your brand. And track changes over time. For deeper insights that lead to greater customer satisfaction, retention, and revenue.