Word2Vec is a tool that can be used to find semantic clusters of words that show the relation to the searched word. This gives an opportunity to analyse discourse, relations and use of words making it a powerful tool for students and researchers to use and explore.
The tool is a high-dimensional word embedding based on an unsupervised machine-learning algorithm using a simple neural network. It maps each unique word in a large text corpus to a vector.
The vector representation of the words reflects interesting semantic properties of the words. The most effective method of Word2Vec is to find words that appear in the same context as another word, because it will be close in the vector space. However, distance between words can also be generalised and produce qualified guesses for analogies. The Word2Vec demo features several corpora and a very large one based on over 65.000 Gutenberg E-books containing multiple languages.