What is Text Classification, and how does it work?

Emails, chat sessions, webpages, and social media all contain unstructured content. Nonetheless, unless the data is arranged in a specific way, extracting value from it is difficult.

It used to be a time-consuming and expensive process to do so because it required manually sorting the data.

Text classifiers combined with natural language processing have shown to be a useful approach to organise textual data in a quick, cost-effective, and scalable manner.

What Is Text Classification and How Does It Work?

The practise of categorising text into ordered groupings is known as text classification, sometimes known as text tagging or text categorization. Text classifiers use Natural Language Processing (NLP) to automatically analyse text and assign pre-defined tags or categories depending on its content.

Examples of Text Classification

Text classification is becoming an increasingly significant aspect of enterprises since it enables for easy data analysis and business process automation. The following are some of the most common examples and use cases for automatic text classification:

• Sentiment Analysis is the method of determining if a text is speaking positively or adversely about a particular topic (e.g. for brand monitoring purposes).
  
• The task of determining the theme or topic of a piece of text is known as topic detection (e.g. know if a product review is about Ease of Use, Customer Support, or Pricing when analysing customer feedback).
  
• Language detection refers to the process of determining the language of a text (e.g. know if an incoming support ticket is written in English or Spanish for automatically routing tickets to the appropriate team).
  

What Is a Text Classifier and How Do I Make One?

You can utilise MonkeyLearn, a platform that makes it very easy to design, train, and consume text classifiers, if you don't want to spend too much time learning about NLP, the underlying infrastructure, or deploying classifiers.

Sign up for a MonkeyLearn account and follow these simple steps to create your own classifier:

1.Create a Classifier first.

Click Create a Model on the MonkeyLearn dashboard after logging in. Choose Classifier: Model to indicate the sort of model you wish to generate.

2. Bring your text data into the programme.

You'll then need to import the text you'll be using to train your classifier. You can do so by uploading a CSV or Excel file that looks like this:

Then choose the columns that contain the text examples you wish to use in the classifier's training:

3. Define your Classifier's Tags.

You must now define the tags that will be used by the text classifier. Your model will make predictions for the following categories or buckets:

Tag definition hint

Avoid using unclear or overlapping tags when defining your tags, as this can confuse your classifier and impair its accuracy.

It's also a smart idea to organise your tags and create a hierarchical text classification system. This means you should arrange your tags based on their semantic relationships.

For instance, if you want to categorise product descriptions, you could use the tags Electronics, Computers, Cell Phones, Clothing, and Automotive. Computers and Cell Phones should be subtags of Electronics in this situation because they are a sort of electronics. In this scenario, it's best to design a hierarchical structure using your tags and build two classifiers: one that can classify product descriptions using the top level tags (Electronics, Clothing, and Automotive), and another that can classify using the Electronics subtags (Computers and Cell Phones).

4. Categorize your data

It's time to tag each text example with the proper tags and start training the model now that you've imported your text data and established the tags for your classifier.

By labelling examples, you'll be training the classifier that you expect a specific output (tags) for a specific input (text):

The classifier will learn from your classifications and begin to provide suggestions as you tag examples. This will provide you with immediate feedback on the classifier's prediction accuracy. Remember that the more text you tag, the more accurate the classification becomes.

5. Text Classifier Testing and Improvement

Once you've completed the classifier building wizard, you may test the model by typing text in "Run" > "Demo." You'll be able to see what the forecasts are for the texts you write:

MonkeyLearn gives some valuable statistics (Accuracy, F1 Score, Precision, and Recall) that can assist you in determining how well your classifier predicts:

Check out this helpful post to understand what to do if you want to increase these metrics and your classifier's overall performance.

6. Using the Classifier to Analyze New Data

You can use the classifier to examine and categorise additional unseen text once the predictions are good enough. Batch processing, APIs, and interfaces are all options available through MonkeyLearn.

To classify text in a batch, you can submit a CSV or Excel file:

The classifier will evaluate the text data and provide a new file with the classifications added to the original file in a new column once you've uploaded the file.

Another method is to programmatically classify text using the API and your choice programming language:

Alternatively, you may connect MonkeyLearn with hundreds of applications to classify your text data (no scripting required!) using the various integrations:

Final Thoughts

Text categorization isn't just entertaining; it's also a useful technique for retrieving information from unstructured data.

When you analyse thousands of messages in a matter of seconds and retrieve information like topic, sentiment, or language, it feels like magic.

Sign up for MonkeyLearn for free or schedule a demo to get started right now!