If you've read our article on the benefits of text analytics, then you already have an idea of how businesses use this powerful tool to uncover patterns and insights from unstructured data.

But what about sentiment analysis? How does it fit into the picture?

While both techniques revolve around analyzing text, they serve unique purposes and often complement each other in surprising ways.

In this article, we’ll

• break down the key differences between text analytics and sentiment analysis,
• explore how they work together, and
• show you how businesses are using them to enhance customer experiences and drive smarter decisions.

Ready to dive deeper? Let’s get started!

Definition and Scope of Text Analytics

Let’s start with what text analytics is. It’s the process of extracting insights from unstructured textual data—like customer feedback, social media posts, or product reviews—to uncover patterns and trends that drive better decisions.

Unlike structured data, which is neatly organized, unstructured data is messy, and analyzing it requires large language models (LLM), generative AI, or natural language processing (NLP). It’s no surprise that this capability has become essential as 80% of enterprise data is unstructured, including everything from emails to online reviews.

The scope of text analytics is incredibly broad. It can process text and find common concerns expressed in them. It helps identify what customers truly think about their products or services.

Text analytics helps in coding qualitative data for businesses to

• uncover valuable insights from customer sentiments,
• identify recurring complaints, and
• even predict future trends.

By doing so, text analytics gives companies opportunities to refine their products and services to boost customer satisfaction. based on customer preferences.

Definition and Purpose of Sentiment Analysis

Sentiment analysis, also known as opinion mining, is a technique that also uses LLM, machine learning, or natural language processing to assess the emotional tone behind textual data. It categorizes text as positive, negative, or neutral.

In simple terms, it’s a way to gauge the mood of customers by analyzing their words. So businesses can know if customers are happy or not about their products or services.

Here’s a peek at how it works: If a customer leaves a review saying, “The delivery was late, but the product quality is great,” sentiment analysis can break it down into both positive (product quality) and negative (delivery delay) aspects.

With this information, companies can better understand what their customers love and where they need to improve.

Sentiment analysis has proved useful in multiple industries. In healthcare, for one, studies found it to have achieved an impressive 81.8% accuracy in detecting emotions from interview data.

Using advanced AI for faster processing, automated sentiment analysis helps businesses finding out the voice of the customer with ease. With real-time results, they can act fast on customer concerns.

Text Analytics vs Sentiment Analysis: Key Differences

Text analytics and sentiment analysis may seem similar, but they serve distinct purposes and use different approaches.

Text analytics is a broader method that uncovers patterns, trends, and actionable insights in data. It categorizes large volumes of textual data into themes, identifying areas for improvement. Sentiment analysis, on the other hand, zooms in on the emotional tone of the text, determining whether feedback is positive, negative, or neutral.

Focus and Scope

• Text Analytics: Focuses on identifying themes and patterns in textual data using methods like parts of speech and word frequency analysis.
• Sentiment Analysis: Specializes in understanding the emotional tone, helping businesses assess how customers feel about specific topics.

Data Types

• Text Analytics: Processes exclusively textual data and can work with feedback such as video, audio, and images after preprocessing (e.g., transcription or OCR).
• Sentiment Analysis: While primarily text-focused, it extends to emotional analysis of feedback such as video, audio, and images, using AI to detect tone, pitch, and facial expressions.

Applications in Business

• Text Analytics: Helps businesses identify recurring themes in customer reviews, such as "shipping delays" or "product design," guiding strategic improvements.
• Sentiment Analysis: Focuses on customer satisfaction by highlighting emotional feedback trends, such as positive sentiment about quick delivery or negative sentiment about customer support.

For a detailed comparison of quantitative + qualitative approaches, check out Using AI to Theme Qualitative Data. This differentiation builds a strong foundation for understanding how these methods work independently.

Text Analytics and Sentiment Analysis Complement Each Other

While text analytics and sentiment analysis operate differently, their combined power provides businesses with holistic insights. Text analytics identifies what customers are talking about—themes and patterns—while sentiment analysis reveals how they feel about those themes.

How They Work Together

• Text analytics categorizes large volumes of feedback into actionable themes like "customer support" or "delivery delays."
• Sentiment analysis evaluates the emotional tone behind these themes, showing whether customers are frustrated, satisfied, or neutral.

Real-Life Synergy

Imagine a retail company analyzing product reviews:

• Text Analytics: Groups feedback into categories such as "packaging," "price," and "customer service."
• Sentiment Analysis: Highlights whether sentiments about "packaging" are positive, such as "sleek and eco-friendly," or negative, like "prone to damage."

By integrating both methods, businesses can move beyond surface-level insights to put together proactive solutions that drive customer satisfaction and loyalty.

To learn more about integrating these tools, check out our guide on customer review analysis.

Text Analytics and Sentiment Analysis in Action

To showcase how text analytics and sentiment analysis work, here are some business that placed their trust in Thematic:

1. Text Analytics at Vodafone

Using text analytics, Vodafone processed mountains of textual data, categorizing it into themes like "billing," "network quality," and "customer support." Once they found recurring patterns, Vodafone prioritized systemic issues, improving customer satisfaction and reshaping its customer experience strategy. In doing so, the company was able to focus resources effectively and apply meaningful changes in its operations.

Read the full case study here.

2. Sentiment Analysis at DoorDash

DoorDash used sentiment analysis to evaluate the emotional tone of customer reviews. They centered on topics like delivery speed and order accuracy. In the findings, they were able to pinpoint negative sentiments, such as frustrations about late deliveries, and take immediate corrective action.

Over time, these improvements boosted both customer satisfaction and operational efficiency. Ultimately, the company enhanced its competitive edge in the food delivery market.

Learn more about DoorDash's customer understanding approach.

3. Text Analytics at LendingTree

Like Vodafone, LendingTree also used text analytics to decode customer feedback. They focused on improving clarity in their loan offerings.

By categorizing customer comments into themes like "application process," "loan terms," and "customer service," LendingTree found common frustrations. One major insight was customer confusion about interest rates. Having found this, LendingTree redesigned communication materials for better transparency.

In the end, the number of customer complaints dropped, and their conversion rates grew as borrowers felt more confident about their decisions.

Explore the LendingTree case study.

4. Sentiment Analysis at Atom Bank

Atom Bank also used sentiment analysis to monitor customer emotions in real-time as they interacted with the bank’s digital platforms. From their analysis of feedback on the user interface, onboarding process, and app functionality, Atom Bank found satisfaction and pain points.

For example, there was positive sentiment related to ease of account creation, while negative sentiment was found for navigating loan applications. Based on these insights, Atom Bank made iterative improvements to its digital experience. Eventually, they boosted customer loyalty.

Read more about Atom Bank's success story

Harness the Power of Text Analytics and Sentiment Analysis

Text analytics and sentiment analysis work hand-in-hand to give your business a comprehensive toolkit for understanding and acting on customer feedback. Just imagine it: if you can figure out themes and uncover emotional insights, wouldn’t it be easier to make informed, data-driven decisions and enhance customer experiences?
Ready to transform your feedback into actionable insights? Get a demo of Thematic today and discover how these powerful tools can revolutionize your customer experience strategy.