Text Classification

Text Classification – Importance, Use Cases, and Process

Data is the superpower that is transforming the digital landscape in today’s world. From emails to social media posts, there is data everywhere. It is true that businesses have never had access to so much data, but does having access to data enough? The rich source of information becomes useless or obsolete when it is not processed.

Unstructured text can be a rich source of information, but it will not be useful to businesses unless the data is organized, categorized, and analyzed. Unstructured data, such as text, audio, videos, and social media, amounts to 80 -90% of all data. Moreover, barely 18% of organizations are reportedly taking advantage of their organization’s unstructured data.

Manually sifting through terabytes of data stored in the servers is a time-consuming and frankly impossible task. However, with the advancements in machine learning, natural language processing, and automation, it is possible to structure and analyze text data quickly and effectively. The first step in data analysis is text classification.

What is Text Classification?

Text classification or categorization is the process of grouping text into predetermined categories or classes. Using this machine learning approach, any text – documents, web files, studies, legal documents, medical reports, and more – can be classified, organized, and structured.

Text classification is the basic step in natural language processing that has several uses in spam detection. Sentiment analysis, intent detection, data labeling, and more.

Possible Use Cases of Text Classification

Possible Text Classification Use Cases There are several benefits to using machine learning text classification, such as scalability, speed of analysis, consistency, and the ability to make quick decisions based on real-time conversations.

  • Monitor Emergencies

    Text classification is used extensively by law enforcement agencies. By scanning social media posts and conversations and applying text classification tools, they can detect panic conversations by filtering for urgency and detecting negative or emergency responses.

  • Identify ways to promote brands

    Marketers are using text classification to promote their brands and products. Businesses can serve their customers better by monitoring user reviews, responses, feedback, and conversations about their brands or products online and identifying the influencers, promoters, and detractors.

  • Data handling made easier

    The burden of handling data is made easier with text classification. Academia, researchers, administration, government, and law practitioners benefit from text classification when the unstructured data is categorized into groups.

  • Categorize Service Requests

    Businesses manage a ton of service requests every day. Manually going through each to understand their purpose, urgency and delivery is a challenge. With AI-based text classification, it is easier for businesses to tag jobs based on category, location, and requirement, and organize resources effectively.

  • Improve the website user experience

    Text classification helps analyze the product’s content and image and assign it to the right category to improve the user experience while shopping. Text classification also helps identify accurate content on the sites such as news portals, blogs, E-Commerce stores, news curators, and more.

Reliable Text Annotation Services to train ML Models.

When the ML model is trained on AI that automatically categorizes items under pre-set categories, you can quickly convert casual browsers into customers.

Text Classification Process

The text classification process starts with pre-processing, feature selection, extraction, and classifying data.

Text Classification Process


Tokenization: Text is broken down into smaller and simpler text forms for easy classification. 

Normalization: All text in a document needs to be on the same level of comprehension. Some forms of normalization include, 

  • Maintaining grammatical or structural standards across the text, such as the removal of white spaces or punctuations. Or maintaining lower cases throughout the text. 
  • Removing prefixes and suffixes from words and bringing them back to their root word.
  • Removing stop words such as ‘and’ ‘is’ ‘the’ and more that do not add value to the text.

Feature Selection

Feature selection is a fundamental step in text classification. The process is aimed at representing texts with the most relevant feature. Feature selections help remove irrelevant data, and enhance accuracy. 

Feature selection reduces the input variable into the model by using only the most relevant data and eliminating noise. Based on the type of solution you seek, your AI models can be designed to choose only the relevant features from the text. 

Feature Extraction

Feature extraction is an optional step that some businesses undertake to extract additional key features in the data. Feature extraction uses several techniques, such as mapping, filtering, and clustering. The primary benefit of using feature extraction is – it helps remove redundant data and improve the speed with which the ML model is developed. 

Tagging Data to Predetermined Categories

Tagging text to predefined categories is the final step in text classification. It can be done in three different ways,

  • Manual Tagging
  • Rule-Based Matching
  • Learning Algorithms – The learning algorithms can further be classified into two categories such as supervised tagging and unsupervised tagging.
    • Supervised learning: The ML model can automatically align the tags with existing categorized data in supervised tagging. When categorized data is already available, the ML algorithms can map the function between the tags and text.
    • Unsupervised learning: It happens when there is a dearth of previously existing tagged data. ML models use clustering and rule-based algorithms to group similar texts, such as based on product purchase history, reviews, personal details, and tickets. These broad groups can be further analyzed to draw valuable customer-specific insights that can be used to design tailored customer approaches. 

There are multiple use cases for text classification across industries. Although gathering, grouping, classifying, and extracting valuable insights from text data has always been used in several fields, text classification is finding its potential in marketing, product development, customer service, management, and administration. It is helping businesses gain competitive intelligence, market and customer knowledge, and make data-backed business decisions. 

Developing an effective and insightful text classification tool is not easy. Still, with Shaip as your data—partner, you can develop an effective, scalable, and cost-effective AI-based text classification tool. We have tons of accurately annotated and ready-to-use datasets that can be customized for your model’s unique requirements. We turn your text into a competitive advantage; get in touch today.

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