code: def positive_negative_word_count(nlp_doc):
      positive_count = 0
      negative_count = 0
      for token in nlp_doc:
          if (
              token.lemma_ in positive_words
              or token.text in positive_words
          ):
              positive_count += 1
          elif (
              token.lemma_ in negative_words
              or token.text in negative_words
          ):
              negative_count += 1
  
      return {
          "positive_word_count": positive_count,
          "negative_word_count": negative_count,
      }
  
file_path: responsible-ai-toolbox/nlp_feature_extractors/nlp_feature_extractors/attribute_extractors.py
imports: import pkg_resources
  from negspacy.negation import Negex
  from raiutils.dataset import fetch_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: positive_negative_word_count(nlp_doc) and returns: dict