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 all trailing and leading punctuation
  If the resulting string has two or fewer characters,
  then it was likely an emoticon, so return original string
  (ie ":)" stripped would be "", so just return ":)"
docstr_tok: ["Removes","all","trailing","and","leading","punctuation","If","the","resulting","string","has","two","or","fewer","characters",",","then","it","was","likely","an","emoticon",",","so","return","original","string","(","ie","``",":",")","''","stripped","would","be","``","''",",","so","just","return","``",":",")","''"]
code: def _strip_punc_if_word(token):
      """
      Removes all trailing and leading punctuation
      If the resulting string has two or fewer characters,
      then it was likely an emoticon, so return original string
      (ie ":)" stripped would be "", so just return ":)"
      """
      stripped = token.strip(string.punctuation)
      if len(stripped) <= 2:
          return token
      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 2 for calling the SentiText obj's underlying member method code with example usage: obj._strip_punc_if_word(token) and returns: stripped, token