input: 
  from typing import List
  
  
  def classify_sentiment_text(texts: List[str]) -> List[str]:
      """
      Given a list of texts, this function classifies each text into 'positive', 'negative', or 'neutral' sentiment.
      The sentiment classification is based on the frequency of positive and negative sentences within the text.
      If the number of negative sentences is greater than the number of positive sentences, the sentiment is 'negative'.
      If the number of positive sentences is greater than the number of negative sentences, the sentiment is 'positive'.
      If the number of positive and negative sentences is equal, the sentiment is 'neutral'.
      >>> classify_sentiment_text(["I am feeling great today!", "The weather is terrible today."])
      ['positive', 'negative']
      >>> classify_sentiment_text(["This is a neutral statement.", "Another neutral statement."])
      ['neutral', 'neutral']
      """
      
  
output:     sentiment_labels = []
      for text in texts:
          positive_count = sum(1 for sentence in text.split('.') if 'great' in sentence or 'good' in sentence)
          negative_count = sum(1 for sentence in text.split('.') if 'terrible' in sentence or 'bad' in sentence)
          if positive_count > negative_count:
              sentiment_labels.append('positive')
          elif negative_count > positive_count:
              sentiment_labels.append('negative')
          else:
              sentiment_labels.append('neutral')
      return sentiment_labels