class_docstr: Yoon Kim and Owen Zhang. Implementation of Credibility Adjusted Term Frequency: A Supervised Term Weighting
  Scheme for Sentiment Analysis and Text Classification. WASSA 2014.
  http://www.people.fas.harvard.edu/~yoonkim/data/cred-tfidf.pdf
class_docstr_tok: ["Yoon","Kim","and","Owen","Zhang",".","Implementation","of","Credibility","Adjusted","Term","Frequency",":","A","Supervised","Term","Weighting","Scheme","for","Sentiment","Analysis","and","Text","Classification",".","WASSA","2014.","http",":","\/\/www.people.fas.harvard.edu\/~yoonkim\/data\/cred-tfidf.pdf"]
code: def _set_scorer_args(self, **kwargs):
      self.eta = kwargs.get("eta", 1.0)
      self.use_sublinear = kwargs.get("use_sublinear", True)
      self.use_cred = kwargs.get("use_cred", True)
      self.use_l2_norm = kwargs.get("use_l2_norm", True)
  
file_path: scattertext/scattertext/termscoring/CredTFIDF.py
imports: import math
  import pandas
  from scipy.stats import norm
  from scattertext.termscoring.CohensDCalculator import CohensDCalculator
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
  import numpy
  from scipy.sparse import csr_matrix
  import math
  import pandas
  from scipy.stats import norm
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
  import numpy
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 3 for calling the CredTFIDF obj's underlying member method code with example usage: obj._set_scorer_args() without return types