input: Write a function to perform sentiment analysis on product reviews by using natural language processing techniques like tokenization and n-grams to generate features for sentiment analysis and use a classification model to assign sentiments to the reviews. The function should read the data from a CSV file named 'reviews.csv', preprocess the reviews, train a Naive Bayes classifier, and print a classification report.
  assert sentiment_analysis("reviews.csv")=="precision    recall  f1-score   support\n\n           0       0.85      0.89      0.87       100\n           1       0.91      0.87      0.89       150\n\nan average     weighted     0.88      0.88      0.88       250\n"
  assert sentiment_analysis("reviews_empty.csv")=="There is no data to analyze."
  assert sentiment_analysis("reviews_with_missing_columns.csv")=="The dataframe has missing columns: review, sentiment."
output: 
  import pandas as pd
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.metrics import classification_report
  
  def sentiment_analysis(filename):
      try:
          df = pd.read_csv(filename)
      except pd.errors.EmptyDataError:
          return "There is no data to analyze."
      
      required_columns = ['review', 'sentiment']
      if not all(col in df.columns for col in required_columns):
          missing_columns = [col for col in required_columns if col not in df.columns]
          return f"The dataframe has missing columns: {', '.join(missing_columns)}"
      
      X = df['review']
      y = df['sentiment']
      
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X_vec = vectorizer.fit_transform(X)
      
      model = MultinomialNB()
      model.fit(X_vec, y)
      
      y_pred = model.predict(X_vec)
      report = classification_report(y, y_pred)
      
      return report
  