'Creates LSA concept vectors by reducing the vector space\'s dimensionality.
Each concept vector has the given number of features (concepts).
The concept vectors are consequently used in Model.cosine_similarity(), Model.cluster()
and classification. This can be faster for high-dimensional vectors (i.e., many features).
The reduction can be undone by setting Model.lsa=False.'

def latent_semantic_analysis(self, dimensions=NORM):

    self._lsa = LSA(self, k=dimensions)
    self._cos = {}
    return self._lsa
