class_docstr: Give a sentiment intensity score to sentences.
class_docstr_tok: ["Give","a","sentiment","intensity","score","to","sentences","."]
code: def _special_idioms_check(valence, words_and_emoticons, i):
      words_and_emoticons_lower = [
          str(w).lower() for w in words_and_emoticons
      ]
      onezero = "{0} {1}".format(
          words_and_emoticons_lower[i - 1], words_and_emoticons_lower[i]
      )
  
      twoonezero = "{0} {1} {2}".format(
          words_and_emoticons_lower[i - 2],
          words_and_emoticons_lower[i - 1],
          words_and_emoticons_lower[i],
      )
  
      twoone = "{0} {1}".format(
          words_and_emoticons_lower[i - 2],
          words_and_emoticons_lower[i - 1],
      )
  
      threetwoone = "{0} {1} {2}".format(
          words_and_emoticons_lower[i - 3],
          words_and_emoticons_lower[i - 2],
          words_and_emoticons_lower[i - 1],
      )
  
      threetwo = "{0} {1}".format(
          words_and_emoticons_lower[i - 3],
          words_and_emoticons_lower[i - 2],
      )
  
      sequences = [onezero, twoonezero, twoone, threetwoone, threetwo]
  
      for seq in sequences:
          if seq in SPECIAL_CASES:
              valence = SPECIAL_CASES[seq]
              break
  
      if len(words_and_emoticons_lower) - 1 > i:
          zeroone = "{0} {1}".format(
              words_and_emoticons_lower[i],
              words_and_emoticons_lower[i + 1],
          )
          if zeroone in SPECIAL_CASES:
              valence = SPECIAL_CASES[zeroone]
      if len(words_and_emoticons_lower) - 1 > i + 1:
          zeroonetwo = "{0} {1} {2}".format(
              words_and_emoticons_lower[i],
              words_and_emoticons_lower[i + 1],
              words_and_emoticons_lower[i + 2],
          )
          if zeroonetwo in SPECIAL_CASES:
              valence = SPECIAL_CASES[zeroonetwo]
  
      # check for booster/dampener bi-grams such as 'sort of' or 'kind of'
      n_grams = [threetwoone, threetwo, twoone]
      for n_gram in n_grams:
          if n_gram in BOOSTER_DICT:
              valence = valence + BOOSTER_DICT[n_gram]
      return valence
  
imports: import os
  import re
  import math
  import string
  import codecs
  import json
  from itertools import product
  from inspect import getsourcefile
  from io import open
label_desc: Use image node_id 8 for calling the SentimentIntensityAnalyzer obj's underlying member method code with example usage: obj._special_idioms_check(valence, words_and_emoticons, i) and returns: valence