  The task of written language identification involves typically the detection
of the languages present in a sample of text. Moreover, a sequence of text may
not belong to a single inherent language but also may be mixture of text
written in multiple languages. This kind of text is generated in large volumes
from social media platforms due to its flexible and user friendly environment.
Such text contains very large number of features which are essential for
development of statistical, probabilistic as well as other kinds of language
models. The large number of features have rich as well as irrelevant and
redundant features which have diverse effect over the performance of the
learning model. Therefore, feature selection methods are significant in
choosing feature that are most relevant for an efficient model. In this
article, we basically consider the Hindi-English language identification task
as Hindi and English are often two most widely spoken languages of India. We
apply different feature selection algorithms across various learning algorithms
in order to analyze the effect of the algorithm as well as the number of
features on the performance of the task. The methodology focuses on the word
level language identification using a novel dataset of 6903 tweets extracted
from Twitter. Various n-gram profiles are examined with different feature
selection algorithms over many classifiers. Finally, an exhaustive comparative
analysis is put forward with respect to the overall experiments conducted for
the task.
