input: Write a python function to classify text sentiment using RandomForestClassifier.
  assert classify_sentiment("The food is amazing!") == "Positive sentiment"
  assert classify_sentiment("I hate waiting in long lines.") == "Negative sentiment"
  assert classify_sentiment("The movie was okay, not great but not terrible.") == "Negative sentiment"
output: def classify_sentiment(text):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.pipeline import make_pipeline
      import numpy as np
  
      # Sample data
      texts = [
          "I love this product!", 
          "This is the worst thing I've ever bought.", 
          "Absolutely fantastic experience.", 
          "I hate it.", 
          "Not bad, could be better.",
          "Very satisfied with the purchase.",
          "Terrible service, will not come back.",
          "Just okay, nothing special.",
          "Amazing quality, would buy again.",
          "Horrible, do not recommend."
      ]
      sentiments = [
          "Positive sentiment", 
          "Negative sentiment", 
          "Positive sentiment", 
          "Negative sentiment", 
          "Negative sentiment",
          "Positive sentiment",
          "Negative sentiment",
          "Negative sentiment",
          "Positive sentiment",
          "Negative sentiment"
      ]
      
      # Vectorizer and classifier pipeline
      model = make_pipeline(TfidfVectorizer(), RandomForestClassifier(n_estimators=100, random_state=42))
      
      # Train the model
      model.fit(texts, sentiments)
      
      # Predict the sentiment of the given text
      prediction = model.predict([text])
      
      return prediction[0]