class_docstr: Creates a topic model from a set of key terms based on sentence level co-occurrence.
class_docstr_tok: ["Creates","a","topic","model","from","a","set","of","key","terms","based","on","sentence","level","co-occurrence","."]
code: def get_sentence_word_mat(self):
      return self.sentX.astype(np.double).tocoo()
  
file_path: scattertext/scattertext/topicmodel/SentencesForTopicModeling.py
imports: import numpy
  import pandas
  from sklearn.decomposition import NMF
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.pipeline import Pipeline
  from scattertext.CSRMatrixTools import CSRMatrixFactory
  from scattertext.ParsedCorpus import ParsedCorpus
  from scattertext.termscoring.RankDifference import RankDifference
label_desc: Use image node_id 2 for calling the SentencesForTopicModeling obj's underlying member method code with example usage: obj.get_sentence_word_mat() and returns: self