class_docstr: This class contains TF-IDF model and LSA model of text-text similarity.
class_docstr_tok: ["This","class","contains","TF-IDF","model","and","LSA","model","of","text-text","similarity","."]
code: def text_similarity(self, t1, t2, model="tfidf"):
      if model == "tfidf":
          t1_vec = matutils.any2sparse(self.text2tfidf(t1))
          t2_vec = matutils.any2sparse(self.text2tfidf(t2))
          return matutils.cossim(t1_vec, t2_vec)
      else:
          t1_vec = matutils.any2sparse(self.text2lsa(t1))
          t2_vec = matutils.any2sparse(self.text2lsa(t2))
          return matutils.cossim(t1_vec, t2_vec)
  
imports: from numpy import array
  from gensim import matutils
label_desc: Use image node_id 7 for calling the TextRelatedness obj's underlying member method code with example usage: obj.text_similarity(t1, t2, model) and returns: matutils, matutils