class_docstr: Citation: Anne-Kathrin Schumann. 2016. Brave new world: Uncovering topical dynamics in the ACL Anthology
  reference corpus using term life cycle information. In Proceedings of the 10th SIGHUM Workshop on Language
  Technology for Cultural Heritage, Social Sciences, and Humanities, pages 1–11, Berlin, Germany. Association
  for Computational Linguistics.
  
  term_scorer = (MannWhitneyU(corpus).set_categories('Positive', ['Negative'], ['Plot']))
  
  html = st.produce_frequency_explorer(
      corpus,
      category='Positive',
      not_categories=['Negative'],
      neutral_categories=['Plot'],
      term_scorer=term_scorer,
      metadata=rdf['movie_name'],
      grey_threshold=0,
      show_neutral=True
  )
  file_name = 'rotten_fresh_mwu.html'
  open(file_name, 'wb').write(html.encode('utf-8'))
class_docstr_tok: ["Citation",":","Anne-Kathrin","Schumann",".","2016",".","Brave","new","world",":","Uncovering","topical","dynamics","in","the","ACL","Anthology","reference","corpus","using","term","life","cycle","information",".","In","Proceedings","of","the","10th","SIGHUM","Workshop","on","Language","Technology","for","Cultural","Heritage",",","Social","Sciences",",","and","Humanities",",","pages","1\u201311",",","Berlin",",","Germany",".","Association","for","Computational","Linguistics",".","term_scorer","=","(","MannWhitneyU","(","corpus",")",".set_categories","(","'Positive","'",",","[","'Negative","'","]",",","[","'Plot","'","]",")",")","html","=","st.produce_frequency_explorer","(","corpus",",","category='Positive","'",",","not_categories=","[","'Negative","'","]",",","neutral_categories=","[","'Plot","'","]",",","term_scorer=term_scorer",",","metadata=rdf","[","'movie_name","'","]",",","grey_threshold=0",",","show_neutral=True",")","file_name","=","'rotten_fresh_mwu.html'","open","(","file_name",",","'wb","'",")",".write","(","html.encode","(","'utf-8","'",")",")"]
code: def get_scores(self, *args) -> pd.Series:
      return self.get_score_df()["Delta"]
  
file_path: scattertext/scattertext/termscoring/Productivity.py
imports: import pandas
  import numpy
  from scattertext.TermDocMatrixWithoutCategories import TermDocMatrixWithoutCategories
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
label_desc: Use image node_id 4 for calling the ProductivityScorer obj's underlying member method code with example usage: obj.get_scores() and returns: self