input: Write a python function to create a word polarity dictionary from a dataset with text and labels.
  assert create_word_polarity_dict(data)['happy'] > 0
  assert create_word_polarity_dict(data)['sad'] < 0
  assert len(create_word_polarity_dict(data)) > 0
output: def create_word_polarity_dict(data):
      from collections import defaultdict
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      
      stop_words = set(stopwords.words('english'))
      
      word_polarity = defaultdict(int)
      for text, label in data:
          words = word_tokenize(text.lower())
          filtered_words = [word for word in words if word.isalnum() and word not in stop_words]
          for word in filtered_words:
              if label > 0:
                  word_polarity[word] += 1
              elif label < 0:
                  word_polarity[word] -= 1
      
      return word_polarity