  Word embedding or vector representation of word holds syntactical and
semantic characteristics of a word which can be an informative feature for any
machine learning-based models of natural language processing. There are several
deep learning-based models for the vectorization of words like word2vec,
fasttext, gensim, glove, etc. In this study, we analyze word2vec model for
learning word vectors by tuning different hyper-parameters and present the most
effective word embedding for Bangla language. For testing the performances of
different word embeddings generated by fine-tuning of word2vec model, we
perform both intrinsic and extrinsic evaluations. We cluster the word vectors
to examine the relational similarity of words for intrinsic evaluation and also
use different word embeddings as the feature of news article classifier for
extrinsic evaluation. From our experiment, we discover that the word vectors
with 300 dimensions, generated from "skip-gram" method of word2vec model using
the sliding window size of 4, are giving the most robust vector representations
for Bangla language.
