class_docstr: This implements wrapper for gensim lsa and tfidf analysis of text collection
code: def load(
      cls, text_process, model="tfidf", top_N=100, save_dir="data/"
  ):
      import logging
  
      logging.basicConfig(
          format="%(asctime)s : %(levelname)s : %(message)s",
          level=logging.INFO,
      )
      dictionary = corpora.Dictionary.load(save_dir + "dictionary")
      tfidf = models.TfidfModel.load(save_dir + "tfidf.model")
      tfidf_index = similarities.Similarity.load(
          save_dir + "tfidf_index/tfidf.index"
      )
      tfidf_index.num_best = top_N
      if model == "lsa":
          lsa = models.LsiModel.load(save_dir + "lsa.model")
          lsa_index = similarities.Similarity.load(
              save_dir + "lsa_index/lsa.index"
          )
          lsa_index.num_best = top_N
          return cls(
              text_process,
              model,
              dictionary,
              tfidf,
              tfidf_index,
              lsa,
              lsa_index,
          )
      return cls(
          text_process,
          model,
          dictionary,
          tfidf,
          tfidf_index,
          None,
          None,
      )
  
file_path: sematch/sematch/semantic/analysis.py
imports: from gensim import corpora, models, similarities, matutils
label_desc: Use image node_id 5 for calling the TextAnalysis obj's underlying member method code with example usage: obj.load(cls, text_process, model, top_N, save_dir) and returns: cls, cls