Do you collect heaps of customer feedback but struggle to make sense of it? Maybe you’ve heard about tools like text analytics but aren’t sure how they work or where to start.

You’re not alone!

This beginner’s guide will walk you through how text analytics works, from gathering raw feedback to uncovering the trends that drive smarter decisions.

Why does this matter? By 2025, 70% of businesses are expected to use machine learning and natural language processing (NLP) to decode customer sentiment—a critical piece of text analytics. Understand how it works, and turn overwhelming data into happy customers.

But before that, let’s clarify what text analytics is: It’s the process of transforming unstructured text into actionable insights using techniques like topic modeling and sentiment analysis.

Now that it’s clear, let’s dive into the process!

1. Data Collection

Of course, the first thing your text analytics tool will do is collect data.  But where?

These are where you want to get your data:

• Product reviews: What do customers love or hate about your product?
• Voice of Customer (VoC): Surveys and interviews reveal what people are really saying.
• Social Media: Tweets and posts can be a goldmine of real-time feedback.
• Support Emails: These often hold clues about common issues.

Keep these in mind:

The Challenge: This data doesn’t arrive in neat rows and columns. It’s messy, unstructured text—think paragraphs of customer opinions instead of tidy spreadsheets.

The Solution: Tools like Thematic analysis software can turn this into structured data. They automatically categorize feedback by topics for seamless analysis.

Once data is in, what do you do with it? Your text analytics tool will have to clean it.

2. Text Preprocessing

Data gathered will be cluttered and, hence, difficult to analyze. Your text analytics tool will perform the text process–cleaning and preparing the raw data so it’s ready for analysis.

This process is like sorting dirty laundry before washing—the system is not yet deciding which items are more important. It’s just ensuring the data is in the right shape and free of noise.

Tasks in text preprocessing include:

• Tokenization: Breaking sentences into words or phrases, called tokens.
• Stemming: Simplifying words to their root forms (e.g., "running" becomes "run").
• Part-of-Speech Tagging: Assigning POS-tagged tokens to phrases, identifying nouns, verbs, and more to understand sentence structure.

By applying methods like rules-based algorithms to tokenize text, the system breaks text into smaller, manageable pieces. This helps advanced techniques like deep learning algorithms, which rely on well-structured data, to perform effectively.

Ultimately, the tool is just organizing data, not necessarily grouping or interpreting them yet.

Now, your data is primed. Time to classify them!

3. Text Classification

Naturally, you’d want to make sense of the data, so next in how text analytics works is text classification. This means the text analytics tool will group data into meaningful categories.

If preprocessing is like sorting laundry into "lights" and "darks," classification is when the system decides where those clean clothes go—"work clothes," "gym clothes," or "casual wear."

The goal: Reveal patterns and themes from the cleaned data by organizing them into actionable categories.

Techniques for Text Classification

• Rule-based methods rely on predefined rules. For example, a rule might classify any feedback mentioning “price” or “cost” under “pricing issues.” While straightforward, these systems can struggle with nuance.
• Machine learning models use algorithms to detect patterns and classify text automatically. They are particularly effective for handling diverse and complex feedback.

Take note however that, simpler models often work well for alphabetic languages but logographic languages require the use of complex machine-learning algorithms to account for their symbol-based structure and context. Note that:

• examples of alphabetic languages are English and Spanish. They rely on letters and relatively straightforward word structures.
• examples of logographic languages are Chinese and Japanese. They use symbols to represent entire words or ideas.

So instead of drowning in feedback, you get a clear picture of what’s working and what’s not. With such knowledge, it’s easier to make better decisions and keep customers happy.

Just look at the case of Smith & Smith. They used Thematic to categorize customer feedback themes linked to NPS, drastically reducing manual work and improving their focus on areas needing attention.

Time to understand how your customers feel.

4. Sentiment Analysis

You now know what your customers are saying, but how do they truly feel? Sentiment analysis uncovers the emotions behind the texts you gathered. Are your customers delighted? Frustrated? Indifferent?

That means you don’t just know that "delivery" is a common concern; you also know if customers are thrilled with the fast service or upset about delays.

Here’s what it does:

• Detecting Sentiment: Feedback is categorized as positive, negative, or neutral based on the language used. For example, "This app is amazing!" gets tagged as positive, while "It keeps crashing" would be negative.
• Sentiment Scoring: More advanced tools assign a score to feedback, measuring the intensity of the emotion. A score of +10 might reflect strong satisfaction, while -10 signals extreme dissatisfaction.

Sentiment analysis helps close the feedback loop—a cycle where feedback is collected, analyzed, and used to improve products or services. As such, businesses can perform customer review analysis, spot recurring complaints, or highlight features that customers love, and then act on them accordingly.

A great example is Melodics, a music-learning platform. They used sentiment analysis to refine their product based on customer feedback, significantly improving customer satisfaction.

Now, you know how they feel, but aren’t you curious about what’s driving their emotions? That’s up next.

5. Topic Modeling

Topic modeling uses text analytics techniques to group similar pieces of feedback into themes. For example, comments mentioning "delivery delays," "late packages," or "missed deadlines" might all be grouped under a theme like "delivery issues."

Unlike text classification, which assigns feedback to predefined categories (like "pricing issues" or "customer service"), topic modeling is more exploratory. It discovers hidden patterns in your data without needing labels upfront.

If text classification is about sorting into known folders, topic modeling is like uncovering unexpected trends in your feedback pile, such as "reliable delivery times" or "outdated app features."

Topic modeling played a key in uncovering product issues for Levels. This health and wellness company used topic modeling to analyze survey responses. From the common theme of customers’ concerns, they refined their strategy and improved customer satisfaction.

Using AI to theme qualitative data makes this process simpler, so you can identify recurring topics with precision.

Now, you probably want to know where all these concerns are coming from. Let’s continue then.

6. Entity Recognition

Now that you’ve uncovered themes in your feedback, the next step is entity recognition—digging even deeper to extract specific details like names, locations, products, or other key elements from the text.

If topic modeling is about seeing the big picture, entity recognition zooms in on the finer details.

Using text analytics and natural language processing (NLP), entity recognition identifies important "entities" within a text. For example:

• Names: Extracting mentions of customers, employees, or brands.
• Locations: Identifying cities or regions discussed in feedback.
• Products or Services: Spotting specific product names or service features.

These tools often rely on part of speech tagging, which helps the system understand whether a word is a noun, verb, or adjective, providing context for accurate identification.

If you were running a text analysis on customer reviews from your social media, and topic modeling revealed themes like "slow delivery" or "great product quality," and sentiment analysis found out customers are frustrated or thrilled, entity recognition will take this a step further. It will pinpoint the "who," "what," or "where" behind these themes.

For example:

• If negative feedback frequently mentions "New York branch", it could indicate that slow delivery is a recurring issue specific to that location.
• If positive reviews often highlight the "vegan burger", it shows this product is delighting customers and could be promoted or expanded further.

Why is that important? Because then you can create targeted improvements. You know you need to improve the New York branch or promote the vegan burger. Simply put, you can prioritize efforts where they matter most.

Now, how do you present this data to your team or client? Visuals would be the best!

7. Data Visualization

It’s time to make it easy to understand data and act on insights. Enter data visualization. It transforms complex insights into visual formats that anyone can grasp at a glance.

Most text analytics tools offer this feature, but why does it matter?

Imagine trying to explain trends in customer feedback using just numbers or paragraphs. It’s hard to see the big picture, right?

So when we talk about data visualization, we mean graphs, charts, heatmaps, and dashboards. These visuals present information in a simple and impactful way.

Imagine looking at these:

• A line graph showing how customer satisfaction has improved over time.
• A heatmap highlighting which locations or products generate the most feedback—positive or negative.
• Interactive displays summarizing key metrics, like recurring themes, sentiment trends, or frequently mentioned entities.

Data visualization makes it easier to transform feedback into business intelligence that drives smarter, faster decisions.

8. Real-Time Analytics and Integration

Making the text analysis insights work for you in real-time is vital. After all, what good is a beautiful dashboard if you can’t act on its findings the moment they matter most?

That’s where real-time analytics and integration with other tools come into play. If you see changes on the charts as soon as they take place, wouldn’t it be able to address issues fast?

But sometimes one tool won’t be enough. Integrations would be vital as well. If your tool can’t integrate with customer review platforms or social media, where will it get data?

The power of real-time analytics grows even further when integrated with business intelligence (BI) tools. For example:

• Combine text analytics data with operational dashboards in tools like Power BI or Tableau.
• Use integrations to link customer feedback directly to internal processes, like supply chain adjustments or customer service alerts.

Thematic integrates with Power BI and Tableau as well as with:

• Slack or Teams: Automated alerts notify teams when specific feedback patterns emerge.
• Survey Platforms: Integrations with Qualtrics or SurveyMonkey pull in live feedback as it’s received.

With real-time insights and seamless integration, your business is always one step ahead.

Thematic

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Wrapping It Up: How Text Analytics Works

So, you’ve seen how text analytics works—it collects data, cleans it, classifies it, identifies your customers’ emotions, locates the source of issues, and presents everything in a visually appealing way.

Text analytics does all the hard work for you so you can make smart decisions.

But what are the real-world impacts?

The benefits of text analytics are clear: better customer understanding, faster decision-making, and the ability to uncover trends that can transform your business.Want to experience the power of text analytics for yourself? Experience text analytics in action on your own data with a demo of Thematic.