Gender Prediction in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
This work addresses the problem of author profiling for researchers in computational linguistics dealing with code-mixed data, but it is incremental as it builds on existing methods applied to a new dataset.
The paper tackles gender prediction in English-Hindi code-mixed social media content by creating an annotated Twitter corpus and developing a baseline system using machine learning algorithms with character and word-level features, achieving results that demonstrate the feasibility of the approach but without specifying concrete performance numbers.
The rapid expansion in the usage of social media networking sites leads to a huge amount of unprocessed user generated data which can be used for text mining. Author profiling is the problem of automatically determining profiling aspects like the author's gender and age group through a text is gaining much popularity in computational linguistics. Most of the past research in author profiling is concentrated on English texts \cite{1,2}. However many users often change the language while posting on social media which is called code-mixing, and it develops some challenges in the field of text classification and author profiling like variations in spelling, non-grammatical structure and transliteration \cite{3}. There are very few English-Hindi code-mixed annotated datasets of social media content present online \cite{4}. In this paper, we analyze the task of author's gender prediction in code-mixed content and present a corpus of English-Hindi texts collected from Twitter which is annotated with author's gender. We also explore language identification of every word in this corpus. We present a supervised classification baseline system which uses various machine learning algorithms to identify the gender of an author using a text, based on character and word level features.