Text Classification: 6 Use Cases You Can Try for Your Industry

This blog post builds on our insights gained while creating a text classification model that provides seamless access to categorized news and boosts engagement within the client's app.

 

Today, businesses process more text data than ever before. And your company is no exception. 

 

Customer reviews, emails, social media posts, documents, surveys, and other such data are invaluable to any business making data-driven decisions.

 

However, due to its sheer diversity, analyzing and making sense of textual data can be pretty intricate.

 

Modern businesses generate more than 2.5 quintillion bytes of text data every day. This is worth re-reading! 

 

Now, imagine how tedious and time-consuming it would be to manually analyze and manage this truly mind-boggling amount of data. It’s a huge task.

 

This is exactly where text classification methods come in.

 

Using machine learning, you can automate the text classification process, gain valuable insights, enhance customer service, and improve your business operations.

In this article, the Onix specialists:

 

• explore popular text classification algorithms you can apply
• demonstrate some real-world text classification applications, giving you an idea of how your business can start to benefit from this technology too.

 

Well, let's start!

Key Takeaways

 

• Around 80-90% of business data is unstructured. Such data is expanding faster than organized data, and it's hard to get valuable insights from it. 

 

• The text analytics market is projected to reach $41.2 billion in 2032.

 

• Today, businesses should build text classification algorithms to analyze unstructured data effectively, gain valuable customer insights, and streamline operations. Companies can quickly identify trends and sentiments, leading to better decision-making and faster responses to customer needs.

 

Text Classification in Business: Use Cases and Applications

Text classification is a machine learning technique used to categorize text into predefined categories, making it a powerful tool in business for managing large amounts of unstructured data.

 

With the rise of digital communication, companies deal with:

 

• customer reviews
• emails
• documents
• social media content daily requiring efficient processing and organization.

 

Below, we explore popular text classification algorithms you can apply:

 

Sentiment analysis

Sentiment analysis is one of the common ways to automate text classification and better understand the feelings expressed in a piece of text. It can determine if the tone is positive, negative, or neutral.

 

Here are some ways to use sentiment analysis:

 

• Customer support. Analyze customer inquiries and feedback to measure satisfaction and improve service quality.

 

For example, Airbnb applies sentiment analysis to assess feedback from hosts and guests. Thus, the company can get an insight into the business, detect negative sentiments about specific properties or services, and manage them proactively. 

 

Below, you can see a flowchart showing sentiment analysis of Airbnb reviews. This process starts by collecting the reviews dataset and ends by calculating the values of classification metrics.

The flowchart illustrates Airbnb's sentiment analysis process

 

• Marketing. Evaluate campaign effectiveness by analyzing customer response and sentiments on social media and review platforms.

 

• Brand monitoring. For example, H&M tracks brand mentions across social media platforms to detect potential PR issues early, respond quickly to negative sentiment, and maintain a positive brand image.

 

• Finance. Analyze news articles and reports to assess market sentiment and predict stock movements based on public opinion.

 

• Healthcare. Evaluate patient feedback and satisfaction from surveys and reviews to enhance services and patient care.

 

Read also: Image Classification: 6 Industries & 26 Use Cases You Can Try

 

Language detection

Language detection is another example of text classification that can automatically identify the language of a given text.

 

Identifying a text's language enables systems to apply language-specific models and pipelines, improving the accuracy of downstream tasks such as sentiment analysis, topic categorization, and spam filtering.

 

Language detection ensures that texts are routed correctly. For example,

 

• a customer support system can automatically detect the language of an incoming message and forward it to an appropriate team or chatbot fluent in that language.
• social media monitoring platforms can classify content correctly, applying localized sentiment analysis or topic tagging to different regions.

 

By applying this technology, you can:

 

• streamline communication
• improve customer experience
• support global operations.

Language detection feature developed by Onix

 

Here are some business areas where language detection can be applied:

 

• Ecommerce. You can automatically detect the language of product reviews, customer feedback, or inquiries from international shoppers to offer personalized responses or recommendations.

 

“The ability to suggest relevant products based on individual preferences is the cornerstone of modern eсommerce. It transforms shopping into a personalized experience, driving engagement and sales."

 

- Oleksandr Hergardt, Head of ML department.

 

For example, a popular platform like AliExpress uses language detection to automatically translate product details, customer reviews, and messages between buyers and sellers. This makes the shopping experience more inclusive and accessible.

 

• Customer support. You can automatically detect the language of customer queries or complaints in emails, chats, or support requests to redirect them to specialists who know that language.

 

• Social media monitoring. Monitor and categorize social media posts by language to analyze global brand perception and customer sentiment across geographies.

 

• Content personalization. Detect the language preferences of website visitors or app users to deliver personalized content, including articles, product descriptions, and UI elements.

 

For example, the popular streaming service Netflix makes things easier by customizing the user interface, subtitles, and audio settings to fit users' language choices. The system checks the language you’ve set on your device or profile, and then Netflix recommends content in your preferred language. 

 

• News and media. Detect the language of news articles, blogs, or user-generated content to categorize and deliver region-specific or language-specific news feeds.

 

Media companies like BBC or CNN provide personalized content recommendations and global news in the language of their audience.

Spam detection and email filtering

By implementing text classification, you can automatically detect and block harmful emails. The system checks incoming messages to see if they're real or spam, helping keep your business safe from phishing, scams, and unwanted emails. 

 

• Financial services. Banks and financial institutions use spam detection to filter phishing emails and protect sensitive customer data from fraud or cyber-attacks, ensuring secure communication channels.

 

JPMorgan Chase uses advanced spam detection to protect sensitive financial data. Their system detects phishing attempts, stopping scammers from impersonating bank representatives and protecting everyone from dangerous emails.

 

• Legal services. Law firms use email filtering to protect client communications from phishing attempts or unsolicited emails, ensuring the confidentiality and integrity of legal correspondence.

 

• Ecommerce. Automatically filter out spam messages from suppliers or customers, ensuring only legitimate emails reach the team. This helps improve responsiveness and protect customer relationships.

Topic labeling for content management

This is another form of text classification technology that automatically tags and categorizes content based on its subject or theme. 

 

It plays a crucial role in content management systems, helping businesses, websites, and digital platforms classify and structure data for various applications, such as search engines, recommendation systems, and analytics.

 

Here are some advantages of automatic text classification, namely topic labeling for content management:

 

• helps to organize large amounts of text, such as articles, blogs, or documents, by labeling them with relevant topics.
• makes it easier for businesses to manage, search, and deliver personalized content to users.

 

Let’s consider how to use text classification:

 

• Education. Khan Academy sorts its educational videos and exercises by subjects like "Math," "Science," and "Arts," making it easy for students to jump straight to the topics they need.

Example of topic labeling for content management

 

• Ecommerce. Use topic labeling to categorize product descriptions, reviews, and marketing content by topic (like "furniture" or "pet supplies"). This topic labeling helps customers easily find what they’re looking for through searches or filters.

 

• Publishing and media. Automatically tag and categorize articles, news stories, or blog posts based on their topics. This helps streamline content organization, improve searchability, and enable personalized recommendations.

 

BuzzFeed, a premier digital media company, uses AI to organize its articles and quizzes into categories like "Zodiac," "Love," or "Harry Potter." Such classification allows users to find the content they are interested in, increasing engagement and the desire to spend more time on the site.

Example of topic labeling to categorize quizzes by their subjects

 

Customer feedback trends

Customer feedback trends involve analyzing vast amounts of feedback data and automatically sorting it into themes, sentiments, or specific topics.

 

This process helps businesses:

 

• understand what customers love or dislike,
• identify recurring issues,
• make informed decisions to enhance services or products and improve customer satisfaction.

 

Here are some popular business areas where you can apply customer feedback trends:

 

• Ecommerce. You can analyze customer reviews and ratings to identify trends in product satisfaction.

 

For example, Zalando uses text classification technology to sort and analyze customer feedback quickly. Thus, they can quickly respond to customer issues, improving their shopping experience and product offerings.

 

• Healthcare. Healthcare providers can monitor patient feedback through surveys and reviews to detect trends in patient care, treatment effectiveness, and overall satisfaction.

 

• Hospitality. Hotel and restaurant owners can use customer feedback from reviews and surveys to identify trends in service quality and guest satisfaction. This helps them make informed improvements to enhance their guests' experiences.

 

Read also: How AI Can Transform Your Business

 

Online content moderation

Online content moderation uses algorithms to automatically review and categorize content like comments, posts, or reviews according to set criteria such as sentiment or relevance.

 

Reasons to consider text classification:

 

• quickly identify harmful content, such as hate speech, spam, or misinformation, and take necessary actions like removing or flagging it.
• maintain a safe and respectful online environment while allowing human moderators to focus on more complex issues.

 

Here are some key areas:

 

• Social media platforms. Facebook uses advanced algorithms to monitor content and quickly remove posts that include hate speech, misinformation, or inappropriate visuals.

How Facebook uses AI to moderate content

 

• Video sharing platforms. YouTube uses machine learning to spot inappropriate comments and videos that violate its rules. By analyzing the sentiment of comments, the platform can automatically filter out harmful or offensive language, which helps create a safer space for users.

 

• Online forums and communities. Websites like Reddit use machine learning and AI tools to flag inappropriate or harmful content before users see it automatically. These tools help catch spam, harassment, and mature content, allowing moderators to keep the platform safe for everyone.

 

• Customer support channels. Companies can utilize online chatbots and customer support platforms to moderate incoming messages. Text classification helps identify urgent issues, common questions, and negative sentiments, allowing for efficient responses and improved customer satisfaction. 

 

Learn more: How to Build Teams for AI Projects

 

Although we’ve considered only some typical applications of text classification in various business areas, there's no doubt that the impact of this technology is incredible.

 

To help you see even more benefits, we’ve created a table comparing critical efficiencies between traditional methods and practical text classification applications.

 

 

"An AI PoC acts as a prototype that showcases potential functionalities and uncovers hidden challenges. It’s like a rehearsal before the grand performance, ensuring everything is in place."

 

– Oleksandr Hergardt, Head of ML Department. 

How Onix Can Help: Our Experience and Services

Onix built the LSTM News Categorizer, a centralized news aggregator designed to classify news into ten predefined topics. 

 

Business context: As the amount of available information grows, users often find it challenging to sift through and discover content that truly matters to them. Recognizing this struggle, our client saw the need for a platform that organizes news articles, making it easier for users to browse and consume relevant information.

News categorization with LSTM models built by Onix

 

The solutions we developed:

 

• LSTM models. The Onix team used LSTM (Long Short-Term Memory) neural networks to categorize news articles, achieving a validation accuracy of over 0.93.
• Language detection. This feature recognizes the language of news articles, allowing you to personalize the sorting and organization of content.
• Robust database. We built a solid system to collect and process news articles, integrating multiple sources for a diverse news pool.
• User interface. Our designers created a visually appealing web view interface that allows customization and filtering options, increasing user experience.

 

Key business goals achieved:

 

• Significant increase in user engagement, with 40,000 active users in the client's user base.
• The LSTM-based model achieved a validation accuracy of over 0.93, ensuring precise classification of news articles.
• A scalable system can handle growing content from diverse news sources.

 

Ready to implement text classification?

 

Our ML development services include:

 

• ML consulting to show you how to leverage ML in your industry.
• ML development to guide you through planning, testing, launching, and maintaining your ML solutions.
• Research and technological dev to choose the best technology and devices for your ML products.
• Maintenance and support to upgrade your ML products based on real user feedback and fine-tuning performance.
• Prototyping and proof of concept to validate critical features and assess the feasibility of your ideas.
• ML integration to securely connect data sources and select the best-suited ML model for your requirements.

 

In any case...

FAQ

 

How does text classification differ from other AI technologies? 

Text classification differs from other AI technologies in that it focuses specifically on organizing and categorizing text into predefined categories based on its content.

 

Unlike AI systems that work with images, audio, or numerical data, text classification relies on natural language processing to analyze and interpret the meaning of words and phrases.

 

What are the benefits of text classification for my business?

Text classification can streamline operations by automating customer feedback analysis, content moderation, and spam detection tasks. It helps businesses improve decision-making, enhance customer experience, and efficiently process large amounts of data.

 

What types of data can be classified using text classification?

Text classification can categorize various types of textual data, including customer reviews, social media posts, emails, support tickets, and documents. It is used to classify feedback, detect spam, filter content, analyze sentiments, and organize large volumes of text.

 

This technology is commonly applied in areas like eCommerce, customer service, marketing, and online forums, where text data is abundant and needs to be organized or analyzed efficiently.

 

How scalable is text classification for growing businesses?

Text classification is highly scalable for growing businesses because it uses automated algorithms to process large volumes of text data efficiently, regardless of the dataset's size.

 

Can text classification be integrated into my existing business systems?

Yes, text classification can be integrated into existing business systems, including CRMs, e-commerce platforms, and customer service solutions. Custom APIs and software solutions can be built to seamlessly add this functionality without disrupting current workflows.

 

What are the challenges of implementing text classification?

Challenges include selecting suitable algorithms, ensuring data accuracy, managing large datasets, and handling complex language nuances. A clear strategy and an experienced ML team can help mitigate these issues.

 

How long does it take to implement a text classification system?

The time it takes to implement a text classification system can vary depending on factors like the complexity of the use case, the volume of data, and the desired accuracy. For simpler applications with pre-built models, implementation can take a few weeks, while more complex, customized systems may take several months. 

 

Can Onix help businesses implement text classification solutions?

Yes, Onix has the expertise to help businesses implement text classification solutions. From analyzing business needs to developing and integrating custom text classification models, Onix can provide end-to-end support for successful implementation.