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GuidesCustomer researchA guide to text analysis techniques with use cases

A guide to text analysis techniques with use cases

Last updated

8 May 2023

Reviewed by

Cathy Heath

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Text analysis is a powerful tool for understanding the meaning behind written language. It can be used to uncover hidden insights and gain actionable data from large amounts of text.

By using text analysis to analyze large amounts of data, businesses can gain a deeper understanding of customer sentiment, product performance, and more.

In this guide, discover how to use text analysis to aid customer services and marketing communications. Find out how it can help you gain insights into customer sentiment, leading to data-driven decisions. Learn the steps to take when analyzing text and the tools available to help you get started.

What is text analysis?

Text analysis is a technique for uncovering insights from data sources such as text documents, web pages, emails, and social media posts. It enables companies to gain actionable data and better understand the sentiment of their customer or potential customer.

The technique combines linguistics and computer science to derive meaning from text sources. It’s sometimes referred to as text mining or text analytics, but these terms have slightly different meanings. Text mining focuses on extracting key information from large amounts of text, while text analytics involves analyzing text data to extract meaningful insights.

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Why is text analysis important?

Text analysis is essential for businesses that are looking to uncover insights, understand customer sentiment, and gain actionable data. The technique can accurately locate patterns, trends, and relationships in large amounts of text data that may otherwise be hard to detect.

With text analysis, businesses can

  • Better understand customer needs and preferences.

  • Optimize customer service experiences.

  • Uncover potential customer segments and market opportunities, enabling them to stay ahead of competitors.

  • Develop strategies that drive business growth, such as making informed pricing strategies and product development decisions.

  • Create targeted marketing campaigns.

  • Identify potential risks.

  • Improve customer loyalty and retention.

Let’s look at three key uses for text analysis in more detail.

  • Sentiment analysisanalyzing customer sentiment with text analysis helps businesses understand how customers feel about their product or service. Suppose a customer leaves a review on an online store and mentions they like the product. Sentiment analysis can detect the positive comment and note that the customer is satisfied.

  • Record management—text analysis is used to identify potential records in unstructured text data. This allows businesses to easily locate and organize records. Text analysis can process customer feedback from an online survey and detect key topics. Then, it will store the results in an organized way.

  • Personalizing customer experience—text analysis is also used to personalize customer experiences by understanding customer needs and preferences.

Text analysis is becoming crucial for businesses as it helps them gain crucial insights. The tools and techniques used in text analysis are constantly evolving, making it an essential tool for any company that wants to stay ahead of its competition.

Text analysis for customer service teams

Text analysis tools can enable customer service teams to effectively analyze customer conversations and gain actionable data. These tools provide analytics capabilities such as sentiment analysis, topic modeling, and clustering.

The process derives insights from unstructured text data using algorithms, machine learning, and natural language processing (NLP). This can help customer service teams gain valuable feedback through voice of customer (VoC) reporting. Text analysis also enables customer service teams to create and maintain a knowledge base for frequently asked questions with ease.

By harnessing this technique, customer service teams can uncover customer insights that empower them to deliver personalized and improved experiences. In practice, this might involve detecting high-value customers, anticipating their problems, and implementing automated ticket routing, prioritization, and tagging.

Text analysis for marketing teams

Marketing teams can use text analytics to help them develop more productive campaigns and target audiences more effectively.

Monitoring customer conversations unlocks vital insights into customers’ needs, wants, and expectations. Marketers can gain a better understanding of customer loyalty, predict customer lifetime value, and identify potential customer churn.

The knowledge that text analysis uncovers enables marketers to detect early customer trends and potential lead-to-customer opportunities. They can also determine which strategies are working and which need improvement. Campaigns can be adjusted quickly to capitalize on opportunities as they arise.

Text analysis techniques

Text analysis is an umbrella term for several methods used to interpret, classify, and extract meaning from text data. In this section, we will look at the four most commonly used text analysis techniques:

  1. Text classification is the process of assigning a label or category to a piece of text either manually or through automated algorithms. For example, you could use text classification to group customer feedback into “positive” or “negative” categories.

  2. Text extraction involves extracting specific pieces of information from unstructured text either manually or with an automated tool. For example, text extraction could automatically find customer names and addresses from emails or customer feedback forms.

  3. Topic modeling is a type of text analysis that uses natural language processing to identify topics or themes. The goal is to identify the most critical themes in a piece of text and then analyze how the topics relate to each other.

  4. Personally identifiable information (PII) redaction removes sensitive information from text before it is shared publicly. This could include social security, bank account, and credit card details.

Stages of text analysis

Text analysis uses computers to understand large amounts of text. It uses natural language processing techniques, machine learning, and data science to analyze unstructured text and extract valuable insights.

The four main stages of text analysis are:

1. Data gathering

The first step is to collect data from various sources such as online reviews, social media conversations, website feedback, and emails.

The data needs to be formatted to make it easier to analyze. For example, if you’re analyzing customer reviews, you would have to structure the data into a format that includes the customer name, product type, review rating, and review content.

2. Data preparation

Once the data is gathered, it needs to be prepared for analysis. This involves cleaning the data to remove any irrelevant words or punctuation. You’ll also need to categorize the data based on sentiment, topic, or other criteria.

3. Text analysis

Now that the data is clean and categorized, it’s time to use text analysis tools to uncover insights. Text analysis tools use algorithms to detect patterns and sentiments in the text. Some of these tools can detect topics of conversation or even identify individual people from their comments.

4. Visualization

The last step of text analysis is to visualize the data. This helps make the insights easier to understand and can help provide context.

Visualization tools like charts and graphs can be used to show the relationships between different categories of data or show trends over time.

Text analysis vs. natural language processing

Natural language processing is a branch of artificial intelligence that focuses on understanding human language using computers. NLP enables machines to interpret and analyze natural language, such as spoken or written words, to extract useful information.

On the other hand, text analysis involves analyzing unstructured or semi-structured text data to extract meaningful insights. Businesses can use it to extract facts, figures, and other important information from text that can be used for analysis.

Unlike NLP, text analysis

  • Focuses on analyzing the text to uncover insights, whereas NLP focuses on understanding the meaning behind the text.

  • Is concerned with extracting facts and figures from the text, while natural language processing is more concerned with understanding the text’s underlying context and sentiment.

  • Relies heavily on structured data, while natural language processing requires unstructured data.

  • Doesn’t require the use of advanced algorithms like those used in natural language processing.

  • Is used for tasks like summarizing and classifying documents, while NLP is used for more complex tasks like machine translation, answering questions, and conversational agents.

Essential tools for text analysis

Basic text analysis tools:

  • A customer relationship management (CRM) system is a digital platform that allows businesses to manage customer data, including customer interaction and purchase history. It also helps in tracking customer trends and buying patterns. Salesforce is a popular CRM system used by many companies.

  • Microsoft Excel is a popular spreadsheet program data analysts use to store, organize, and analyze data. It can help perform various types of text analysis, such as sentiment analysis, topic modeling, and keyword extraction.

  • Social listening tools are used to monitor and analyze conversations on social media networks like Twitter, Facebook, and Instagram. They allow you to track customer sentiment, measure your content’s reach, and identify influencers. Hootsuite is an example of a popular social listening tool that many companies use.

Advanced text analysis tools:

  •  Google NLP products are a suite of natural language processing services that make it easy to analyze text for its sentiment, entities, syntax, and more. These tools allow you to take unstructured text data and create structured insights. For example, Google’s Natural Language API can detect the sentiment of a customer review and automatically assign it a score based on how positive or negative it is.

  • Amazon Comprehend is an NLP service designed to help customers quickly and accurately extract meaningful insights from large volumes of text data. It provides sentiment recognition, sentiment analysis, language detection, and keyphrase extraction features. For example, businesses can use Amazon Comprehend to extract customer feedback from online reviews to better understand what customers think about their products or services.

  • Watson Natural Language Understanding is a cloud-based NLP service that allows users to analyze text data and gain insights. It offers sentiment analysis, entity extraction, keyword extraction, concept tagging, and sentiment scoring. Businesses can use Watson Natural Language Understanding to extract customer sentiment from tweets about their product or service, allowing them to identify areas of improvement quickly.

  • Azure Text Analysis API is a cloud-based NLP service that provides features such as sentiment analysis, keyphrase extraction, language detection, and entity extraction. It allows businesses to analyze unstructured text and extract insights from it quickly and cost-effectively. For example, Azure Text Analysis API can generate a sentiment score for customer feedback to quickly assess customer satisfaction levels.

What is the best language for text analytics?

When it comes to text analytics, the language you choose greatly impacts the results you can get. Different languages are best suited to different tasks, so it’s important to know which one will work best for your analysis.

Some of the most popular languages for text analytics include Python, R, and SQL. It’s helpful to understand the strengths and weaknesses of each language before selecting the one best suited to your needs.


Python is an excellent choice because of its versatility and ability to create advanced models. It is well suited to NLP tasks like sentiment analysis and entity extraction.

Extracting customer feedback from social media posts is an example of a task Python can help with.


R is an ideal language for statistical analysis and data visualization. It is used for text mining tasks such as classification, clustering, and topic modeling.

An example of a task R can help with is creating visualizations of customer sentiment across product categories. 


SQL is a powerful language for querying data in databases. It can be used for text analytics tasks such as text classification and search.

For example, SQL can help with performing keyword searches of customer support emails to find relevant topics quickly.


Text analysis is a powerful tool that enables businesses to gain insights, understand customer sentiment, design more targeted campaigns, and generally make more informed decisions.

As an essential part of data-driven decision-making, the technique has become increasingly important as more information is available online. It can help companies stay competitive by giving them an advantage in the ever-changing digital landscape.

The demand for text analysis is only increasing, and businesses that leverage this technology will be well-positioned to remain competitive in their respective markets.

Businesses need to identify the right tools for their specific needs and build processes around them to get the most out of text analysis. While it requires some effort up front, investing time into text analysis now will pay dividends in the future.

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