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Text & Sentiment Analysis: Key Differences & Real-World Examples

With the implementation of AI and ML algorithms, text and sentiment analysis engines are finding their way into different industry applications.

These engines are being used in market research, customer feedback analysis, social media monitoring, and more, enabling businesses to derive valuable insights from vast amounts of textual data in much less time than legacy systems.

Though both text analysis and sentiment analysis are text mining techniques, there are a few fundamental differences between them.

Text analysis focuses on extracting and organizing information from unstructured text, while sentiment analysis goes a step further, aiming to understand the emotional tone or sentiment expressed in the text.

In this blog, we will explore the definitions of text and sentiment analysis, highlighting their similarities and delving into their key differences. 

We have also included real-world examples of how organizations are leveraging these techniques in various applications. So, let’s begin.

What is Text and sentiment analysis: Definitions

Text analysis

Text analysis is the technique of extracting meaningful insights from raw unstructured text to identify a common theme. It lets you understand the meaning behind the text so you can see what is the topic of conversation.

Suppose you have just conducted an NPS survey. Using text analysis on the feedback, you can identify the main talking points in the text.

Let’s say a follow-up question is – How do you feel about the [brand name]?

Using a text analysis technique like Word Could, you can scan the responses to highlight the most repeated phrases and words in the data.

Words such as horrible, not good, and excellent would be highlighted in the ratio of how many times they appear in the responses to give you a sense of data trends.

You can then use the data to follow up with the customers and collect more in-depth insights into their feedback.

There are different types of text analysis techniques:

  • Text Classification: Categorizes the text into predefined classes or categories based on its content or characteristics.
  • Text Extraction: Identifies and pulls out specific pieces of information from a larger text for further analysis or summarization.
  • Word Frequency: Counts how often each word appears in a text or a collection of texts, providing insights into the most common or significant words used.
  • Word Sense Disambiguation: Determines a word’s correct meaning or sense in a given context, as many words can have multiple meanings.
  • Clustering: Groups similar documents or data points together based on their content similarity.

Sentiment Analysis

Sentiment analysis lets you understand the emotion behind the text. The method categorizes the text data based on emotions like negative, positive, neutral, sad, etc. 

Sentiment analysis helps businesses make data-driven decisions, improve customer satisfaction, and identify emerging trends or issues that require attention.

Let’s take the same survey example as above.

Analyzing the same feedback data using sentiment analysis would let you see how respondents feel about each survey question.

Suppose you are processing the responses to the question – How do you feel about the [brand name]?

The sentiment analysis would parse words like bad, frustrated experience, not recommend, etc., to categorize it as negative, sad, angry, etc. Then, you can use it to respond to negative feedback first and improve the customer experience.

Here are some types of sentiment analysis techniques:

  • Graded Sentiment Analysis: Evaluates the sentiment of a text on a scale such as 1 to 5 to show the intensity of the emotion expressed.
  • Emotion Detection: Identifies and categorizes the specific emotions expressed in a text, such as happiness, sadness, anger, fear, etc.
  • Aspect-based Sentiment Analysis: Determines the sentiment using specific entities in the text data to provide a more granular understanding of opinions and feelings related to different aspects.
  • Multilingual Sentiment Analysis: Determines the sentiment in texts written in multiple languages.
Read more: Sentiment Analysis: Guide to Understanding Customer Emotions

The similarities between text and sentiment analysis

The main similarity between the two techniques is the objective. Both are part of text mining methods aimed to process and organize unstructured data into meaningful insights. Plus, they work on NLP and ML to extract the meaning behind the text data.

Find the emotions behind customers’ feedback. Try Qualaroo’s AI-Based IBM Watson

Text Analysis vs. Sentiment Analysis: Key Differences

Here are some key differences between text and sentiment analysis:

Types of analysis

Text analysis helps you to find the themes and trends in the data. You can identify the context of any given text or a large dataset. It picks the words and phrases at face value and helps to provide quantitative insights.

For example, the most trending topics among the customers in their social media posts.

On the other hand, Sentiment analysis extracts the semantic meaning of the data showing you the emotional tone of the piece of text.

The technique helps collect qualitative insights from the given data.

How it works

Another major difference between text and sentiment analysis is how text mining and NLP are applied to parse the data.

Here’s how text analysis works:

The data is first transformed into a standardized format and divided into small chunks called tokens. These may be words, phrases, or sentences. Each token is tagged into its grammatical syntax like noun, verb, or adjective. 

The NLP model then analyzes the relationship between the words to determine the sentence structure. It helps to understand the underlying themes or topics within the data.

Now, let’s see how a typical sentiment analysis engine works:

The first step is isolating the sentiment-bearing words, phrases, or emojis conveying emotions. The engine then uses ML algorithms and predefined sentiment lexicons to classify the sentiment of the text as positive, negative, or neutral.

Advanced engines go further to categorize the sentiment into happiness, sadness, anger, or fear by analyzing the language and context to infer the writer’s emotional state. Finally, the data is tagged into the relevant emotion for further classification.

Though both methods work on the data text, text analysis focuses on understanding the sentence’s literal meaning, while sentiment analysis digs into its emotional tone.


Text Analysis: Text analysis finds its way into various fields, including customer reviews analysis, document clustering, market research, and fraud detection.

Sentiment Analysis: Sentiment analysis is specifically designed to understand public opinion, sentiment trends, and emotional responses. It is heavily used in social media monitoring to track brand reputation, analyze customer feedback, and gauge consumer sentiment toward products or services.

4 Real-World Examples of Text and Sentiment analysis Applications

Let’s dive into some practical examples to understand how companies or tools use text and sentiment analysis to streamline data processing and decision-making.

Belron – Uses Qualaroo’s AI sentiment analysis engine for feedback data

The customer success team at Belron – one of the largest car windshield manufacturers, is fanatic about providing a stellar customer experience.

The team uses Qualaroo’s NPS surveys for collecting customer feedback from online and in-store visitors. 

The data is analyzed using Qualaroo’s built-in text and sentiment analysis engines to extract valuable real-time insights from the raw feedback. It saves the manual effort so the team can focus more on acting on the feedback.

The sentiment analysis processes and categorizes the data into user emotions on a scale of -1(negative) to 1(positive). Additionally, the Word Cloud text analysis engine highlights the key phrases and words to help understand what the customers are talking about in the feedback.

The insights help the team understand the customer journey and find the friction points to streamline the experience for current and future customers.

That’s how Belron maintains a sky-high NPS score of 80.

Build a robust customer feedback loop with Qualaroo. Start Your Trial to explore full features.

Google NLP API

Next in line is Google’s NLP API, a cloud-based data analytics platform that offers both text and sentiment analysis for your data. The data can be search queries, chat histories, customer feedback, social media posts, text documents, or other textual information.

Travel Media Group

Being in the hospitality industry, Travel Media Group relies heavily on word of mouth to promote the brand and attract new customers. 

Observing the sheer amount of user-generated reviews on various social media channels, the company updated its legacy systems for analyzing the data.

They implemented an aspect-based sentiment analysis system at their end. It helped to analyze and track all the social media interactions, including colloquial words and phrases, industry jargon, emojis, code switches, likes, and hashtags.

The team could discover topics and themes of the gathered feedback and use them to enhance the experience further. It also provided rich customer metadata for building ideal customer personas.

FitBit Text Analysis in customer support

Fitbit, one of the largest wearable fitness device producers, tracks customers’ activity on various social media platforms. The company’s Twitter page is flooded with customer reviews, posts, and support team replies.

Considering the sheer volume of customer engagement, the team leveraged advanced text-mining techniques to categorize the complaints. The objective was to track major issues to see whether they are related to activity tracking, design, tech specs, or application interactivity.

First, the data was consolidated into a spreadsheet and then fed into the text analytics engine for further analysis. The system parsed the data to group similar comments and tagged them with relevant topics.

Then, each complaint or issue was scored based on their seriousness and occurrence in the customers’ tweets.

The data helped Fitbit Inc. to identify issues in specific models and resolve them. This study also proved useful in tracking how the newly released products perform and what customers think about new products.

It’s a great way to turn feedback into valuable customer insights.

How to Implement Text and Sentiment Analysis in survey campaigns

Out of the myriad of applications of text and sentiment analysis, we want to spotlight the significance of customer feedback analysis. 

If you’re considering implementing a customer feedback loop through survey campaigns, incorporating text and sentiment analysis is a game-changing approach to swiftly close the feedback loop and gain actionable insights.

Step 1: Get a Suitable Feedback Tool

The foundation of a successful customer feedback loop lies in selecting a suitable survey tool that effectively captures and compiles textual responses. A well-designed survey with open-ended questions encourages customers to share detailed and in-context feedback, facilitating thorough text analysis.

One noteworthy survey tool is Qualaroo, renowned for its user-friendly interface and powerful features. It serves as an excellent choice for conducting surveys that capture the essence of customer feedback.

What sets Qualaroo apart is its integrated AI-based sentiment analysis engine, which streamlines the survey process by automatically analyzing customer sentiments, all without resorting to intrusive promotional language.

Step 2: Design and Run Your Survey Campaign

Crafting a well-structured survey is an art that involves thoughtfully designing questions that elicit meaningful responses. 

By incorporating a mix of rating scales and free-text options, you can acquire both quantitative and qualitative data, enabling comprehensive analysis through text and sentiment techniques.

Step 3: Collect and Consolidate the Data

Once your survey campaign is underway, collecting and consolidating responses in real-time is paramount to maintain data integrity. 

Organizing and managing data efficiently sets the stage for successful text and sentiment analysis, enabling you to draw meaningful insights from the abundance of feedback.

Step 4: Parse the Data through AI Text and Sentiment Analysis Engine

The real magic happens at this step, where AI-driven text and sentiment analysis engines come into play.

Leveraging advanced algorithms, these engines process vast volumes of textual data, extracting patterns, keywords, and emotional tones that help unravel valuable customer sentiments and preferences.

In this regard, Qualaroo’s AI-based sentiment analysis engine excels, as it expertly deciphers emotional nuances in customer feedback.

Its sophisticated approach allows you to identify sentiment trends, prioritize areas of improvement, and make data-driven decisions that resonate with your target audience.

Step 5: Derive an Action Plan to Deal with Different Customer Insights

Armed with the results of text and sentiment analysis, you’ll gain a comprehensive understanding of your customers’ sentiments and pain points. 

Utilize this wealth of information to formulate an actionable plan that addresses various customer insights. 

Whether it involves rectifying issues, optimizing processes, or enhancing products and services, data-driven decisions empower you to continuously improve and deliver exceptional customer experiences.

Complement Text Analysis with Sentiment Analysis For Best Results

Text and sentiment analysis have become indispensable tools in the age of AI and ML algorithms.

Regardless of the size and scope of your business data, these powerful techniques can be implemented to make sense of the information at hand.

By harnessing the capabilities of text and sentiment analysis, organizations can gain valuable insights, enhance customer experiences, and make data-driven decisions, propelling their success in today’s data-centric landscape.

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About the author

Dwayne Charrington is an expert in UX design and user research, showing a strong grasp of how to improve user interfaces and interactions. He explores a wide range of topics, including lead generation, feedback management, the importance of survey accessibility, and how new technologies like AI and VR are changing how users interact with products. He shares insights on creating clear navigation, using A/B testing to make smarter design choices, and the power of storytelling in UX. Dwayne also focuses on optimizing mobile experiences and champions privacy-by-design, ensuring users feel satisfied, secure, and valued.