Lowering Down the Entropy of Words!
“Around 80% of the available data on the Internet is unstructured, with the text being one of the most common types among all.”
According to a study conducted by IBM
Text Classification using NLP plays a vital role in analyzing and organizing unstructured content. With an exponential increase in the problems faced by companies in sorting through text and extracting values from it, Text Classification can be seen as a benefaction. Natural Language Processing (NLP) is enabling Text Classification through Deep Learning Algorithms and this is all that everyone is running after.
Let’s proceed and see how all of it can be achieved just by applying knowledge in the right direction.
What is Text Classification?
Text Classification is one of the most promising applications of NLP. It is defined as the process of categorizing free-text according to its content. Unstructured text is omnipresent in the form of emails, chats, social media, web pages, and whatnot. Today, businesses are turning their head towards text classification for structuring text in an efficient way to automate processes.
For example: Assigning labels to several news articles according to their content can be done by text classifiers easily. The output of this classifier includes categories like Politics, Sports, Entertainment, Business, etc.
How does it work?
First of all, the machine learning model of the text classifier is trained using feature extraction based on past observations. To produce a classification model, a training data set is fed to the algorithm consisting of pairs of features and tags. After training, the model is fed with unseen text to predict which label to apply upon it. In a nutshell, classification algorithms predict the category of testing data sets based on the labels of training data sets.
NLP fostering Text Classification
Let’s have a look over some of the major classification algorithms used for text classification:
Support Vector Machines (SVM): SVM is one of the most important classification algorithms that draw a hyperplane to divide a three-dimensional space into two subspaces, both of them containing completely different attributes from each other. It means the training data set is divided into two different classes and then the new unlabeled test data set is fed to the algorithm. Next, the algorithm determines which subspace the unlabeled data belongs to and subsequently, labels it.
Naive Bayesian classification: It is a family of statistical algorithms based on Bayes’ Theorem and it computes the likelihood of the text belonging to a particular category taking into account the probabilities of the appearance of the words of that text within the texts of that category.
Deep Learning: Familiar with neurons in a human body? They are the basic building blocks of the human nervous system. Likewise, there are artificial neural networks such as CNN and RNN in deep learning which can be used to classify text. They are better than traditional machine learning models and can improve the accuracy of classifiers.
Why are companies leveraging Text Classification using NLP?
Consistent criteria: The same lens for all of the data results in error minimization.
Real-time analysis: Accurate precisions in real-time to identify critical information.
Scalability: Analysis of millions of texts at a fraction of a cost.
Applications of Text Classification
Sentiment Analysis: Text Classification enables the automated process of determining whether a test is positive, negative, or neutral. This process is known as sentiment analysis and can be employed for product analytics.
Topic Labeling: It is all about understanding what a given text is talking about and can be used for organizing customer feedback.
Language Detection: Language detection is the process of classifying incoming text according to its language and can be used for routing purposes.
Intent Detection: Text classifiers are used by the companies for automatically detecting the intent from customer conversations.
Use Cases of Text Classification
Whether you are a product manager or engineer or salesperson, text classifiers render a lot of ways to get things in place for you. Text classification even sometimes works in the background delivering an enhanced user-experience such as filtering spams on email clients. Following are some of the sample use cases of text classification:
Social Media Monitoring: Detecting sales opportunities, identifying social media interactions, detecting potential PR crises.
Brand Monitoring: Categorizing brand mentions to find features, wishes, price, sample use cases, competitors, etc.
Customer Service: Automating several tasks like ticket routing, getting insights from support conversations.
Voice of Customer: Gathering feedback from customers, processing information, addressing concerns.
After all that we have read and seen about text classification, we can easily conclude that it can be your next weapon for building cutting-edge technology and it is incredibly useful for actionable insights from your text that can drive business decisions.
Still, have questions? Feel free to contact us and we will set you up with our best NLP models for text classification.
R, Python, MATLAB, PyTorch, Tensorflow, Azure ML, Amazon SageMaker.
Jupyter lab, Azure Text to Speech API, Google Cloud Text-to-Speech, spaCy, openNLP, PyNLPI, IBM Watson Studio