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"]
code: def __eq__(self, other):
      if other is None or self.__class__ != other.__class__:
          return False
      if (
          self.term != other.term
          if self.term is not None
          else other.term is not None
      ):
          return False
      if (
          self.source_term != other.source_term
          if self.source_term is not None
          else other.source_term is not None
      ):
          return False
      return (
          self.sentence == other.sentence
          if self.sentence is not None
          else other.sentence is None
      )
  
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 3 for calling the CandidateTerm obj's underlying member method code with example usage: obj.__eq__(other) and returns: unknown, False, False, False