class_docstr: Qualified term - term that is accepted to generated lexicon.
  
  Attributes:
      term (list): list of terms
      pos (list): list of pos.
      frequency (int): frequency of filtered term in corpus.
      term_polarity (Polarity): term polarity.
class_docstr_tok: ["Qualified","term","-","term","that","is","accepted","to","generated","lexicon",".","Attributes",":","term","(","list",")",":","list","of","terms","pos","(","list",")",":","list","of","pos",".","frequency","(","int",")",":","frequency","of","filtered","term","in","corpus",".","term_polarity","(","Polarity",")",":","term","polarity","."]
code: def __init__(self, term, lemma, pos, frequency, term_polarity):
      self.term = term
      self.lemma = lemma
      self.pos = pos
      self.frequency = frequency
      self.term_polarity = term_polarity
  
imports: import csv
  from enum import Enum
  from os import PathLike
  from nlp_architect.models.absa import TRAIN_LEXICONS
label_desc: Use image node_id 1 to create a new QualifiedTerm object from inherited base classes: object with example: obj = QualifiedTerm(term, lemma, pos, frequency, term_polarity)