class_docstr: Identify sentiment-relevant string-level properties of input text.
class_docstr_tok: ["Identify","sentiment-relevant","string-level","properties","of","input","text","."]
docstr: Removes leading and trailing puncutation
  Leaves contractions and most emoticons
      Does not preserve punc-plus-letter emoticons (e.g. :D)
docstr_tok: ["Removes","leading","and","trailing","puncutation","Leaves","contractions","and","most","emoticons","Does","not","preserve","punc-plus-letter","emoticons","(","e.g",".",":","D",")"]
code: def _words_and_emoticons(self):
      """
      Removes leading and trailing puncutation
      Leaves contractions and most emoticons
          Does not preserve punc-plus-letter emoticons (e.g. :D)
      """
      wes = self.text.split()
      stripped = list(map(self._strip_punc_if_word, wes))
      return stripped
  
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 3 for calling the SentiText obj's underlying member method code with example usage: obj._words_and_emoticons() and returns: stripped