rampanyam/authorship-profiling-on-twitter-data

This project intends to predict the gender of an author from Twitter data ( Tweets) using Natural Language Processing Techniques and Classification Algorithms.

Authorship-Profiling-on-Twitter-Data

A breif overview of Authorship Analysis:

   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). 

AIM of the Task:

  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.