  Text-based communication is highly favoured as a communication method,
especially in business environments. As a result, it is often abused by sending
malicious messages, e.g., spam emails, to deceive users into relaying personal
information, including online accounts credentials or banking details. For this
reason, many machine learning methods for text classification have been
proposed and incorporated into the services of most email providers. However,
optimising text classification algorithms and finding the right tradeoff on
their aggressiveness is still a major research problem.
  We present an updated survey of 12 machine learning text classifiers applied
to a public spam corpus. A new pipeline is proposed to optimise hyperparameter
selection and improve the models' performance by applying specific methods
(based on natural language processing) in the preprocessing stage.
  Our study aims to provide a new methodology to investigate and optimise the
effect of different feature sizes and hyperparameters in machine learning
classifiers that are widely used in text classification problems. The
classifiers are tested and evaluated on different metrics including F-score
(accuracy), precision, recall, and run time. By analysing all these aspects, we
show how the proposed pipeline can be used to achieve a good accuracy towards
spam filtering on the Enron dataset, a widely used public email corpus.
Statistical tests and explainability techniques are applied to provide a robust
analysis of the proposed pipeline and interpret the classification outcomes of
the 12 machine learning models, also identifying words that drive the
classification results. Our analysis shows that it is possible to identify an
effective machine learning model to classify the Enron dataset with an F-score
of 94%.
