code: def _feature_word_embedding(self, text):
          
          try:
              simils = np.dot(self._prebuilt_vec, text.vector)
          except Exception as e:
              #print("Vector problem, ", Exception, e)
              return {"country_1" : "",
                  "confid_a" : 0,
                  "confid_b" : 0,
                  "country_2" : ""}
          ranks = simils.argsort()[::-1]
          confid = simils.max()
          confid2 = simils[ranks[0]] - simils[ranks[1]]
          if confid == 0 or confid2 == 0:
              return ""
          country_code = self._cts[str(self._ct_nlp[ranks[0]])]
          country_picking = {"country_1" : country_code,
                  "confid_a" : confid,
                  "confid_b" : confid2,
                  "country_2" : self._cts[str(self._ct_nlp[ranks[1]])]}
          return country_picking
docs: Given a word, guess the appropriate country by word vector.
  
          Parameters
          ---------
          text: str
              the text to extract locations from.
  
          Returns
          -------
          country_picking: dict
              The top two countries (ISO codes) and two measures
              confidence for the first choice.