NLP Techniques being Helpful for Spam Detection

NLP techniques are used to train data to detect Spam.

In today’s multimedia-driven world, we’re gathering information and connecting with people has become extremely easy due to social media and the internet. Due to which we get hundreds of messages and emails daily out of which many of them are unwanted. These unwanted messages are called spam and the useful ones are called ham mails. Today we shall see how spam filtration with Natural Language Processing (NLP) is implemented on data to get classified data to train our models to detect spam messages. As the importance of advanced technologies like AI, machine learning, and data science are growing very fast. It’s going to be definitely helpful for AI Startups.

What are spam messages or emails?

Basically, spam is all unwanted messages or emails but are delivered to the user. These spam messages are normally sent by fraud people or for advertising purposes. But the majority of the time they are of no use and it also risks the security of the user’s documents. So it’s very important to detect those spam messages. For which various algorithms are used by evaluating their accuracy so that we can find the perfect fitting algorithm to deliver the desired results.

Spam Filters

With the help of text classifiers, the spam filtration method is applied. Let us look into spam filters,

• Blatant Blocking

It is a process where the emails or messages are deleted even before they are delivered.

• Null Sender Disposition

Here the messages are destroyed if SMTP envelope address of the sender is not mentioned.

• Null Sender Header Tag Validation

Here the security digital signature from each message is confirmed.

Machine Learning tools and algorithms.

Different techniques, tools, and data set which are used here for various purpose. They are listed below,

• Stochastic Gradient descent
• Naive Bayes
• Support Vector Machine
• Logistic Regression
• K- Nearest Neighbours
• Random Forest classifiers
• Decision Tree classification

Dataset

Initial the model is properly trained by providing it with complete data on which supervised learning is to be performed. So that it can reach the desired output of differentiating it between spam or ham. It’s not that simple as various experiments have to be performed on the model with the help of NLP concepts like tokenization, encoding, stemming, stop word removal, feature generation. With all these applications model is able to differentiate datasets properly to deliver desired results.

Hence we can conclude that NLP methods are extremely useful in spam detection.