The ML-model leverages supervised learning and tokenization to clear the spam messages from the mail.
The amount of mails sent and received has significantly increased over the past few years. A report states that more than 300 billion emails were sent and received each day in 2020, and this figure is expected to increase by over 361 billion emails daily in 2024. Spam mails contribute majorly to this exponential increase in mails. And while cleaning the spam messages from the Gmail account might seem tricky, the machine learning model holds more accountability than the traditional method to perform the task.
The prominence of chat apps, subscriptions and incessant promotions mail, are the major reasons with the increase of spam mails. The report estimates that by 2024, the number of global e-mail users gets estimated to grow by 4.48 billion users, up from 3.8 billion in 2018. And while Apple and Google are constantly battling for the spot, there seems to be no remedy to curtail spam mails, via a traditional model. Henceforth, businesses are proactively deploying machine learning models to automate the task of cleaning the mail.
Certainly, a machine learning model must emulate human cognition while dealing with spam mails. By definition, machine learning is a concept having advanced machine learning algorithms, which are composed of many technologies such as deep learning, neural networks and natural language processing. It uses unsupervised and supervised learning to train the datasets for extracting desired information without human intervention.
For addressing the challenges of spam emails, the machine learning model is majorly driven by supervised learning. In supervised learning, the ML algorithm learns to map out the function from input and output variable. The goal is to predict the output in a system with the help of existing trained input datasets. As most of the supervised learning models encourage deploying Bayesian algorithms, experts believe the Naïve Bayes algorithm to be an excellent choice for training a supervised learning model for spam mails. Also known as Bayes Theorem, it is based on the concept of prior knowledge of situations so that the outcomes of similar events can be predicted. Naive Bayes algorithm has been deployed across various fields, but in case of spam detection, the process gets slightly tricky. As the spam mails do not have a framework that distinguishes spam messages from non-spam messages, the datasets need to be trained using specific words and phrases, so that spam mails can easily be flagged out and gets deleted. For example, to curb the spam messages from a food delivery app, the machine learning dataset must be trained using words such as food, delicious, and pizza amongst other similar words. This would help the ML-model to promptly identify the spam mail.
However, a major limitation of ML-model is that they yet do not possess the capability of comprehension, unlike humans. Henceforth, the methodology of tokenization must be taken into account. Tokenization is the method of turning large datasets into smaller meaningful text data, for making the data comprehensive for the model. Additionally, by incorporating the stemming and lemmatization into the dataset vocabulary, the similar and different root words with their respective origin can further simplify the training of ML-model.
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Source: https://www.analyticsinsight.net/how-machine-learning-cleans-spam-messages-from-the-mail/
