Authorship analysis deals with the classification of texts into classes based on the stylistic choices
of their authors. Beyond the author identification and author verification tasks where the style of
individual authors is examined, author profiling distinguishes between classes of authors studying
their sociology aspect, that is, how language is shared by people. This helps in identifying profiling
aspects such as gender, age, native language, or personality type. Author profiling is a problem
of growing importance in applications in forensics, security, and marketing. E.g., from a forensic
linguistics perspective one would like being able to know the linguistic profile of the author of a
harassing text message (language used by a certain type of people) and identify certain characteristics (language as evidence).
Similarly, from a marketing viewpoint, companies may be interested in knowing, on the basis of the analysis of blogs and online product reviews, the demographics of
people that like or dislike their products. The focus is on author profiling in social media since
we are mainly interested in everyday language and how it reflects basic social and personality
processes.
The authorship profiling task is often formulated as a classification problem, where a classifier
is fed with a text and returns the corresponding gender or native language label. There are many
machine learning methods that can be used in the classification task. They can be categorised
into supervised method (like SVM) and unsupervised method (like clustering).
The aim of this challenge is to develop a classifier that can assign a set of twitter texts
to their corresponding labels. In this assessment, you will focus on the gender classification task,
where you are required to develop a classifier that can identify the gender of the tweet’s author as
accurate as possible.