Text classification in NLP

Text Classification is the processing of labeling or organizing text data into groups. It forms a fundamental part of Natural Language Processing. In the digital age that we live in we are surrounded by text on our social media accounts, in commercials, on websites, Ebooks, etc. The majority of this text data is unstructured, so classifying this data can be extremely useful.

Applications

Text Classification has a wide array of applications. Some popular uses are:

• Spam detection in emails
• Sentiment analysis of online reviews
• Topic labeling documents like research papers
• Language detection like in Google Translate
• Age/gender identification of anonymous users
• Tagging online content
• Speech recognition used in virtual assistants like Siri and Alexa

Approaches

Text Classification can be achieved through three main approaches:

1. Rule-based approaches
   These approaches make use of handcrafted linguistic rulesdetermine how text is grouped to classify text. One way to group text is to create a list of words related to a certain column and then judge the text based on the occurrences of these words. For example, words like “fur”, “feathers”, “claws”, and “scales” could help a zoologist identify texts talking about animals online. These approaches require a lot of domain knowledge to be extensive, take a lot of time to compile, and are difficult to scale.

2. Machine learning approaches
   We can use machine learning to train models on large sets of text data to predict categories of new text. To train models, we need to transform text data into numerical data – this is known as feature extraction. Important feature extraction techniques include bag of words and n-grams.
   There are several useful machine learning algorithms we can use for text classification. The most popular ones are:
   

   • Naive Bayes classifiers
     
   • Support vector machines
     
   • Deep learning algorithms

3. Hybrid approaches
   These approaches are a combination of the two algorithms above. They make use of both rule-based and machine learning techniques to model a classifier that can be fine-tuned in certain scenarios.