Seed words are chosen by hand. Either start with a large seed lexicon and depend on induction algorithm to fine tune to its specific domain or use different seed words for different domains

Computation of embeddings for the negative and positive words(pole words),either off the shelf word2vec embeddings or fine tuned embeddings.

Create an embedding that represents each pole by taking the centroid(mean) of the embeddings of each of the seed words.

The semantic axis defined by the poles is computed just by subtracting the pole centroid of positive seed word from the pole centroid of negative seed word.

The semantic axis is a vector in the direction of positive sentiment.

Compute ( cosine similarity) the angle between semantic axis and the direction of w’s embedding. A higher cosine means that w is more aligned with the set of embeddings for the positive seed words than embeddings for the negative seed words.