class_docstr: Candidate opinion term or aspect term.
  
  Attributes:
      term (list): list of terms
      pos (list): list of pos
      source_term (list): list of related anchor terms
      sentence (str): sentence text this term
      term_polarity (int): term polarity
class_docstr_tok: ["Candidate","opinion","term","or","aspect","term",".","Attributes",":","term","(","list",")",":","list","of","terms","pos","(","list",")",":","list","of","pos","source_term","(","list",")",":","list","of","related","anchor","terms","sentence","(","str",")",":","sentence","text","this","term","term_polarity","(","int",")",":","term","polarity"]
docstr: Args:
      term_a (DepRelationTerm): first term
      term_b (DepRelationTerm): second term
      sent_text (str): sentence text
      candidate_term_polarity (Polarity): term polarity
docstr_tok: ["Args",":","term_a","(","DepRelationTerm",")",":","first","term","term_b","(","DepRelationTerm",")",":","second","term","sent_text","(","str",")",":","sentence","text","candidate_term_polarity","(","Polarity",")",":","term","polarity"]
code: def __init__(
      self, term_a, term_b, sent_text, candidate_term_polarity
  ):
      """
      Args:
          term_a (DepRelationTerm): first term
          term_b (DepRelationTerm): second term
          sent_text (str): sentence text
          candidate_term_polarity (Polarity): term polarity
      """
      self.term = [term_a.text]
      self.pos = [term_a.norm_pos]
      self.lemma = [term_a.lemma]
      self.source_term = [term_b.text]
      self.sentence = sent_text
      self.term_polarity = candidate_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 CandidateTerm object from inherited base classes: object with example: obj = CandidateTerm(term_a, term_b, sent_text, candidate_term_polarity)