An examination of whether a more sophisticated learner will always result in better performance in a text-based classifier, and the trade-off between accuracy and training time.
Investigations that provide insights on how to choose an N-gram feature that maximizes performance of a classifier.
In this code story, we explore how the topology of a deep neural network can affect the performance of a text-based classifier.
Utilising deep learning to detect emotions from short, informal English text.
In this code story, we consider how data preparation can impact the performance of a classifier, and how that may lead to a refinement of problem statement, i.e. the important question we are asking.
