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Text Classification
https://monkeylearn.com/text-classification/
Text classification is the process of assigning tags or categories to text according to its content. It’s one of the fundamental tasks in Natural Language Processing (NLP) with broad applications such as sentiment analysis, topic labeling, spam detection, and intent detection.
Unstructured data in the form of text is everywhere: emails, chats, web pages, social media, support tickets, survey responses, and more. Text can be an extremely rich source of information, but extracting insights from it can be hard and time-consuming due to its unstructured nature. Businesses are turning to text classification for structuring text in a fast and cost-efficient way to enhance decision-making and automate processes.
But, what is text classification? How does text classification work? What are the algorithms used for classifying text? What are the most common business applications?
What is text classification?
Text classification (a.k.a. text categorization or text tagging) is the task of assigning a set of predefined categories to free-text. Text classifiers can be used to organize, structure, and categorize pretty much anything. For example, new articles can be organized by topics, support tickets can be organized by urgency, chat conversations can be organized by language, brand mentions can be organized by sentiment, and so on.
As an example, take a look at the following text below:
"The user interface is quite straightforward and easy to use."
A classifier can take this text as an input, analyze its content, and then and automatically assign relevant tags, such as UI and Easy To Use that represent this text:
How Does Text Classification Work?
There are many approaches to automatic text classification, which can be grouped into three different types of systems:
- Rule-based systems
- Machine Learning based systems
- Hybrid systems
Machine Learning Based Systems
Instead of relying on manually crafted rules, text classification with machine learning learns to make classifications based on past observations. By using pre-labeled examples as training data, a machine learning algorithm can learn the different associations between pieces of text and that a particular output (i.e. tags) is expected for a particular input (i.e. text).
The first step towards training a classifier with machine learning is feature extraction: a method is used to transform each text into a numerical representation in the form of a vector. One of the most frequently used approaches is bag of words, where a vector represents the frequency of a word in a predefined dictionary of words.
For example, if we have defined our dictionary to have the following words {This, is, the, not, awesome, bad, basketball}, and we wanted to vectorize the text “This is awesome”, we would have the following vector representation of that text: (1, 1, 0, 0, 1, 0, 0).
Then, the machine learning algorithm is fed with training data that consists of pairs of feature sets (vectors for each text example) and tags (e.g. sports, politics) to produce a classification model:
Once it’s trained with enough training samples, the machine learning model can begin to make accurate predictions. The same feature extractor is used to transform unseen text to feature sets which can be fed into the classification model to get predictions on tags (e.g. sports, politics):
Text Classification Algorithms
Some of the most popular machine learning algorithms for creating text classification models include the naive bayes family of algorithms, support vector machines, and deep learning.
