class_docstr: This implements wrapper for gensim lsa and tfidf analysis of text collection
code: def text2model(self, text):
      t = self._text_process(text)
      bow = self._dictionary.doc2bow(t)
      tfidf = self._tfidf[bow]
      if self._model == "tfidf":
          return tfidf
      else:
          return self._lsa[tfidf]
  
file_path: sematch/sematch/semantic/analysis.py
imports: from gensim import corpora, models, similarities, matutils
label_desc: Use image node_id 2 for calling the TextAnalysis obj's underlying member method code with example usage: obj.text2model(text) and returns: tfidf, self