Text Analysis Examples

Text analysis, also known as text mining, is a machine learning tool used to extract value from text automatically. Before we get to the meat of the matter, let’s put text analysis in context.

The world is in a current state of overdrive when it comes to data – it keeps getting bigger and shows no sign of slowing down any time soon. Think of it this way: approximately 293.6 billion emails are sent every day. Now, consider having to read, process, interpret, tag, and sort a minuscule amount of that volume…let’s say, 500 emails a day. It doesn’t sound like much but trust us, we’re willing to bet good money that you’ll grow tired or bored halfway through, if not sooner.

But why get to the point of exhaustion when we can harness technology to automate tedious tasks?

Let text analysis take these simple tasks off your hands. Train a machine to do the work for you and discover how text analysis can help your business in terms of accuracy and scalability, regardless if you work in customer support, sales, product, or software development.

In this post, we’ll explore and deep-dive into the many (and varied) applications and real-life examples of text analysis, along with the numerous benefits for your business. Not only is it super easy to get started with, but you’ll also discover how straightforward it is to tailor machine learning models to your business.

Take the leap with us and let’s begin our exploration.

What is Text Analysis?

Now, you may be wondering: What exactly is text analysis? Is it is an approach, a methodology, a tool? Short, sweet, and straight to the point, text analysis is the machine learning practice that automates the process of extracting value from text.

Easy enough, right?

Text comes from many, and we mean MANY sources, including social media, emails, survey responses, product reviews, incident and support tickets, and much more. With numerous sources providing massive quantities of unstructured data on a daily basis, it can be incredibly daunting to process and analyze. 

With the help of machine learning tools, you can process and analyze large amounts of data accurately and consistently in no time at all.

Let’s set the scene: You work in the customer service department of a large corporation logging incident tickets. You receive over 1000 tickets via email alone on a daily basis. If you read, categorize, and deliver every single one to the correct department for handling, it would take you a lot longer than a full day of work to get the job done. 

And the chances of that data being fit for analysis are slim since analyzing hundreds and thousands of tickets is tedious, labor-intensive work, and even the most committed employee will grow tired and make mistakes.

Now, let’s get to why we’re really here: text analysis examples and how they can help businesses.

Let’s go over the two main text analysis methods – text classification and text extraction – and the various models available. The one you choose will depend on the insights you are hoping to gain, and/or the problem you’re attempting to solve. Let’s take a closer look:

Text Classification

Text classification, also referred to as text tagging, is the practice of classifying text into pre-defined groups. With the help of Natural Language Processing (NLP), text classification tools are powerful enough to automatically analyze text and classify it into categories, depending on the content that you’re dealing with.

Now, let’s proceed with the different types of text classification models available.

Sentiment Analysis

Nowadays, analysis falls short if it doesn’t examine the different emotions behind every piece of text. Sentiment analysis can automatically detect the emotional undertones embedded in customer reviews, survey responses, social media posts, and so on, which helps organizations understand how their customers feel about their brand, product, or service.

For example, a sentiment analysis of product reviews can help a business understand what customers like or dislike about your product. Think of review sites like Yelp, Capterra, and G2 Crowd, where you might stumble upon feedback about your, let’s say, SaaS business. In the following reviews for Slack, customers praise or criticize a few aspects of the tool: 

In love with Slack, I won’t be using anything to communicate else going forward. How did I survive without it?!” → Positive

I don’t agree with the hype, Slack failed to notify me of several important messages and that’s what a communication platform should be all about.” → Negative

“The UX is one of the best, it’s very intuitive and easy to use. However, we don’t have a budget for the high price Slack asks for its paid plans.” → Neutral

By training a model to detect sentiment on the other hand, you can delegate the task of categorizing texts into Positive, Neutral and Negative, to machines. Not only does this help speed up the process, you’ll receive more consistent results since machines are inherently not biased. 

Topic analysis

Topic analysis is a machine learning technique that interprets and categorizes large collections of text according to individual topics or themes. 

For example, instead of humans having to read thousands of product reviews to identify the main topic that customers are talking about in regards to your product, you can use a topic analysis tool to do it in seconds. 

Let’s say you’re an entertainment on-demand service company that’s just released new content, and you want to know what topics customers are mentioning. You could define tags such as UX/UI, Quality, Functionality, and Reliability, and find out which aspect is being talked about most often, and how customers are talking about each aspect. Take this review for Prime Video:

“I think Amazon is making a great effort in adding engaging content but I can’t get past the ugly interface. It’s not as intuitive as other competing streaming services and if it weren’t lumped in with my Prime membership, I wouldn’t pay for the stand-alone service.”

In this example, the topic analysis classifier can be trained to process this and automatically tag it under UX/UI.

Test MonkeyLearn’s very own feedback classifier for SaaS companies to get an idea of how topic analysis sorts information according to themes.

Language Detection

This clever machine learning model identifies and classifies text according to the language it’s written in. This is particularly helpful to send information to the correct team.

Think of it this way – if your product has a global presence, chances are you’ll probably end up receiving product feedback support tickets in numerous languages. With the help of language detection, language is automatically detected for each text and routed to the appropriate localized teams.

For example, take Joom. Joom is an international e-commerce platform that has a presence in over 150 countries – which means a lot of languages are involved. With a language detection classifier, support tickets can be easily routed to the appropriate support team that can handle each item based on language. See this ticket for example: 

“La blusa es más grande de lo que esperaba, quisiera devolverla por una prenda de una talla menor.”

For this ticket in Spanish, the language detection classifier could easily detect the language which in turn helps to route it to a Spanish-speaking customer service agent who can contact the client and address their review.

Test MonkeyLearn’s language classifier and see how it can identify over 49 different languages!

Intent Detection

Text classifiers can also be used to automatically discover the intention behind text. Intent speaks volumes about a customer’s journey with your company. 

Customers who write to you to ask how to unsubscribe, how to join your newsletter, where to find a specific product on your website, or when an item will be back in stock, all fall into different ‘intent’ categories. For example, you might use the tags Subscribers, Unsubscribers, Interested in Product, etc.

For instance, let’s say you’re reviewing customer surveys for your sporting goods store and find the following response in the “What would you improve about our company?” category:

“Sheesh, the amount of emails I receive is staggering – and it’s only been one week. It’s sporting goods, folks. I don’t need over 20 emails per week to remind me of that. Unsubscribing ASAP.”

With an intent detection classifier in place, you could address this customer immediately and offer them to decrease the number of emails they receive per week, but to not unsubscribe altogether.

With a clear intent detected, such as the one depicted above, you can easily classify customers and take immediate action on how to address each unique situation. In addition, it can help you identify when you need to send a follow-up message, or assist a customer to close a sale.

Play around with the following model that was built specifically to classify outbound sales responses. You’ll get a clear idea of its power. 

Text Extraction

Text extraction is a text analysis technique that identifies and extracts valuable pieces of data from text. It sounds easy enough and with the right tools, such as MonkeyLearn, it absolutely is.

Whether it’s keywords, client names, product characteristics, dates, prices, and any other inherent information that lives within data, text extraction can get the job done. 

Take a closer look at the following examples of text extraction models. 

Keyword Extraction

Keyword extraction is very relevant in a world where customers are openly expressing their opinions across multiple communication channels, from social media and emails to reviews and surveys.

By extracting keywords from texts, you’ll receive an analysis showing the most relevant words or expressions within those texts.

Take political campaigns, for example. By examining Twitter mentions for a specific candidate, you can extract the keywords that are being communicated the most. 

“The traffic jams and number of car accidents in 3rd st cross with Lincoln avenue is a cause for concern. Traffic lights are a must in this part of town. If @JohnSmith promises to fix this immediately, he’s got my vote”

The keyword extractor can automatically detect words and expressions such as traffic jams, car accidents, concern, traffic lights, etc that are representative of what is being talked about in social media. Which could help strategize for a campaign that better addresses specific concerns of people or to prepare a clear, course of action to fix or improve something in a running campaign.

Type your own text into MonkeyLearn’s pre-trained model and see how it work its machine learning magic.

Entity Extraction

Entity extraction is a tool that obtains names of people, companies, brands, and more. This technique is particularly helpful when you’re trying to pinpoint names of competitors, brands, and people with a degree of influence in your business, for example.

Another helpful aspect of entity extraction is to find out specific information in relation to branches. Perhaps your company is global and has a multitude of locations across the world; you could use entity extraction to detect branches that are undergoing particular events, good and bad. 

For example, think about Starbucks. Starbucks has coffeehouses in nearly every corner of the world. With entity extraction, Starbucks could easily pinpoint which locations have more positive Twitter interactions. This sort of information is particularly important for your business because you could investigate what those shops are doing differently and replicate it across every single one.

Play around with this concept by using our pre-trained company entity extractor where you can quickly extract company and organization entities from text in English.

Why is Text Analysis Important?

Anyway you slice it, text analysis will bring value to your business. And said value translates into profits for your company, which you surely don’t want to overlook. 

As we’ve highlighted throughout this post, there are numerous and varied applications from which you can benefit as you employ text analysis into your daily activities. From faster ticket processing and accurate routing of information, to identifying topics and keywords from customer feedback, here are some of the key benefits that text analysis brings to the table:


With lots of data pouring in by the minute, automated text analysis gives you the opportunity to work with colossal amounts of data in a matter of seconds. Since machines work faster than humans, and around the clock, you have the ability to boost performance and scale your tasks. So, even as workloads increase, you won’t need to hire more manpower. Plus, the beauty of text analysis models is that you only need to train them once, as opposed to every time you hire a new member of staff, helping you reduce training costs as well as the amount of time-consuming tasks for existing employees.

Real-time analysis

Text analysis gives you a clear, competitive edge by classifying and extracting text in near real-time, non-stop and 24/7. This means you can identify urgent customer issues in a matter of seconds, and eliminate the complexity and time-consuming efforts of having to analyze text manually.

Consistent criteria

A well trained text analysis model can analyze, interpret, and classify data to deliver consistent and reliable results. Once it has been trained with the correct criteria, it will apply that same criteria to analyze every piece of incoming text – so you can ensure that your results are objective, fair and consistent.

Deep customer understanding

Understanding your customers is crucial to your business’ success. Text analysis gives you valuable insights into what your customers are talking about in regards to your product or service, their feelings towards your brand, how likely they are to purchase from you, and their overall feelings about being or becoming a customer. These insights, some of which are so subtle that they can only be detected by a machine, can help businesses create and tailor strategies that address customer needs in the blink of an eye, providing a superior customer experience.

While machine learning is a subject that poses different degrees of hesitation for potential users, at MonkeyLearn, we can guarantee that it is far easier to use than you can imagine. One, you won’t require a technical background or knowledge about machine learning to use MonkeyLearn, and two, we provide ready-made models that you can get started with right away. 

Examples of Business Applications

Text analysis can automate various processes that help businesses increase their efficiency, improve the overall accuracy of information derived from data, and ultimately, make better decisions. Here, we’re going to focus on two business processes in which text analysis can have a profound effect.

Customer Feedback

Customers vent their feelings about a product, service, or brand via various channels, including social media, review sites, surveys, live chats, and more, giving them the right platform to be more vocal about their like or dislike towards a company. This allows your company to truly listen to its customers, but it also poses the question: how can you analyze and extract value from customer feedback quickly and accurately?

One popular method to measure customer satisfaction is through Net Promoter Score (NPS). NPS measures how likely customers are to recommend you to a friend, and sending out NPS surveys is one of the best ways for companies to understand how customers perceive their product or service. 

Typically, NPS surveys include one simple, yet powerful question: How likely are you to recommend product X? This question comes along with a 0-10 scale, allowing you to categorize survey respondents into Promoters (9-10), Passives (7-8), or Detractors (0-6). The NPS score is the result of subtracting the percentage of Detractors from the percentage of Promoters.

By analyzing the NPS score, you gain a quick glimpse into what customers truly like and dislike about your brand, which is extremely helpful when designing new, more powerful engagement strategies. But you also need to take into account the responses to open-ended questions – which is where the biggest chunk of insights lies. Let’s see why and how.

Text analysis can help you analyze feedback, such as open-ended NPS responses, to give you a clear picture of what your customers want and expect from your brand. It can help you understand why customers are happy or dissatisfied, and which topics they talk about most often. For example, by using a sentiment analysis model you can identify your brand’s standing in terms of positive and negative feedback. Another useful model is a topic classifier, which can categorize information by topic and highlight your customers’ key pain points.

As your company evolves, sentiment analysis can help you spot unexpected negative comments to give you insights about how your customers perceive your product over a period of time, as well as after specific milestones, such as the introduction of new features and products, the removal of a service, and so on. 

Let’s take a look at what happens every time Instagram introduces a new feature – customers leave colossal amounts of feedback via social media and online reviews. Obviously, this is a great source of insights for the company because it lets them know what their customers like or dislike about each new feature, and also provides them with new ideas and suggestions from their customers. However, the only way they are able to handle the large amounts of information they receive is by implementing text analysis learning tools. 

Businesses need to constantly monitor online reviews and social media because most customers rely on them before making a purchase, or even using a free service like Instagram. In fact, 84% of people trust online reviews as much as friends

These online sources are a goldmine of information that can help unearth heaps of knowledge about how your business fares against the competition, what your customers are posting online about you, and their loyalty towards your brand – which are all key indicators of how successful your business is, how you should improve your processes, and how you can assertively target customers.

For example, at MonkeyLearn we analyzed customer support interactions on Twitter from four of the biggest US phone carriers: AT&T, Verizon, Sprint, and T-Mobile. First, we carried out sentiment analysis to group customer comments into Positive, Negative and Neutral, then we used an insight extractor to find out which keywords customers were mentioning within each sentiment category. We discovered a significant amount of interesting insights, which concluded that:

  • T-Mobile has the highest percentage of positive tweets, overwhelmingly so.
  • AT&T is the most mentioned company, with over 64k tweets per week.
  • Verizon receives more negative tweets than positive ones.
  • Every carrier shares the same common complaints — bad customer service, bad reception, and high prices
  • T-Mobile’s positive tweets indicate they’re winning in customer support with friendly and informal interactions.

Customer Service

Ever feel like you’re being flooded with customer queries? By automating customer support, you can swiftly categorize, route, and prioritize issues – all while focusing on more fulfilling tasks that thrive with human involvement.

Customer service is one of the most critical aspects in attaining customer loyalty. Let’s say you are the cardholder of a bank that has high fees but offers outstanding customer service. You’re likely to stay loyal to this bank because you value their premium level of customer service.

However, if customer service starts to diminish over time you might look elsewhere for a bank that provides better value for money. Thus, it is vital to have automated text analysis in place so you can gain insights about how customers feel about the service you’re providing.

You could use sentiment analysis, for example, to detect disgruntled customers, or an urgency detector to find issues that require urgent action. These are the issues you’ll need to prioritize.

Another key benefit that we mentioned earlier is routing queries accurately so they can be dealt with by the most fitting customer support agents. By using topic classification to categorize and route issues to appropriate team members, you can avoid wasting time passing tickets from one agent to the next, until it reaches the correct one, and send customers a faster and more effective response. 

Organizations can also benefit from using text analysis to measure customer satisfaction and locate the specific areas that need improving, as well as measure the individual performance of team members and the overall performance of the company. 

But, how exactly can you measure customer satisfaction based on conversations? Customers who are happy with the way an issue was solved, or frustrated about how long it took to handle their issue, present a great opportunity to identify their customer satisfaction levels.

Through the use of aspect-based sentiment analysis, you can analyze text to categorize it and extract the most talked-about attributes or features of the product or service. In addition, you can also understand if the customer interaction is deemed Positive, Neutral or Negative – and determine the sentiments behind the words and expressions (e.g. sadness, anger, happiness, etc).

How to Create a Custom Text Analysis Model?

If you are keen to get started with text analysis with machine learning, and gain accurate insights from your data, we highly recommend that you build and train your own text analysis model that is designed specifically to meet your unique needs. 

MonkeyLearn makes it easy, fun, and quick to build, train, and use text analysis tools. First, you’ll need to sign up for free, then follow these simple steps:

1. Create a new model

After you sign up, you can access MonkeyLearn’s dashboard and click on create a model. This action will prompt two model type options: Classifier and Extractor. For the purposes of this tutorial, click on the ‘create classifier’ button:

2. Choose the classification type

There are three types of classification available. You can choose topic classification, sentiment analysis, or intent classification. Click on the ‘topic classification’ option:

3. Select and import data

After you select your model, you will be prompted to import data from various sources. You can either upload data in an Excel or CSV file, or you can use one of our many integrations to import your data: Twitter, Gmail, Zendesk, Front, Promoter, Freshdesk, RSS, and Data Library: 

4. Define the tags that your model will use

Tags can be thought of as the different topics that you want your model to focus on. As far as tag definition, we recommend that you create tags that are tailored to your project, specifically addressing the issue that you’d like to solve. For example, if you’re a software development company and you’re looking into classifying customer feedback, your tags might include Reliability, Usability, Functionality, etc. Avoid using tags that are ambiguous, redundant, or confusing to your custom model:

5. Train the topic classifier 

Start training the topic classifier by feeding it samples of data and tagging it with the correct topic:

As you continue tagging text data, the confidence percentage of your model will increase – remember, the more training, the more accurate you’ll model will be.

5. Test your model

Now that you’ve thoroughly trained your model, you can test it by going to the ‘run’ tab and typing a product review or NPS response into the text box, then clicking ‘classify text’ so your model can analyze and make predictions:

Remember, with more training, the confidence percentage will increase.

If you want to continue training your model, you can go to the ‘build’ tab and further train your model until you’re happy with it.

And there you have it, your very own text analysis model using MonkeyLearn’s easy-to-use interface:

6. Put your model to work

Now that your new topic classifier is up and running, you’re ready to start analyzing new data with this model. For this, you have different options:

  • By uploading new data into your model using a CSV or Excel file
  • By using one of the available integrations such as Google Sheets, Zapier, or Zendesk.
  • Or if you know how to code, you can use the model programmatically using MonkeyLearn API

Wrapping up

Text analysis is not only easy to implement into your business processes, but it also brings about numerous benefits, such as speed, accuracy, and scalability – which we’ve covered throughout this post. 

By enabling a machine to perform text analysis for your business instead of humans, you will save time and speed up processes as machines work non-stop. They’re also able to deliver more accurate and consistent results when trained properly, helping you make better business decisions. 

At the end of the day, we all want efficient and effective methods of getting work done – and automating processes via machine learning is the answer. They can take on time-consuming, tedious tasks that free human agents to focus on more important aspects of the business.

Using text analysis tools such as sentiment analysis, topic detection, and urgency identification, you can gain a deeper understanding of your clients, enabling you to create targeted efforts that improve the customer experience in a cost-effective, time-saving fashion. And the more you listen to your customers, the better you’ll be able to make your products and services – helping you gain a competitive edge.

As we mentioned earlier, customer service can benefit greatly from text analysis too, by helping you respond to customers quickly and more effectively, as well as spotting urgent issues that require urgent action. In a world where customers are constantly leaving feedback via various channels, AI models are fast becoming a necessity to help businesses deal with increasing information.

So, what are you waiting for? Request a demo and our team will help you get started with text analysis today, to streamline your processes and turn your data into valuable insights.

Federico Pascual

Federico Pascual

COO & Co-Founder @MonkeyLearn. Machine Learning. @500startups B14. @Galvanize SoMa. TEDxDurazno Speaker. Wannabe musician and traveler.


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