class_docstr: Give a sentiment intensity score to sentences.
class_docstr_tok: ["Give","a","sentiment","intensity","score","to","sentences","."]
code: def _sift_sentiment_scores(sentiments):
      # want separate positive versus negative sentiment scores
      pos_sum = 0.0
      neg_sum = 0.0
      neu_count = 0
      for sentiment_score in sentiments:
          if sentiment_score > 0:
              pos_sum += (
                  float(sentiment_score) + 1
              )  # compensates for neutral words that are counted as 1
          if sentiment_score < 0:
              neg_sum += (
                  float(sentiment_score) - 1
              )  # when used with math.fabs(), compensates for neutrals
          if sentiment_score == 0:
              neu_count += 1
      return pos_sum, neg_sum, neu_count
  
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 14 for calling the SentimentIntensityAnalyzer obj's underlying member method code with example usage: obj._sift_sentiment_scores(sentiments) and returns: pos_sum,  neg_sum,  neu_count