input: Write a function to create a Python program that performs sentiment analysis on product reviews. The program should use 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.
  assert sentiment_analysis_program('reviews.csv') == {report output}
  assert sentiment_analysis_program('empty_reviews.csv') == {report output for empty data}
  assert sentiment_analysis_program('reviews_with_neutral_sentiments.csv') == {report output with neutral sentiments}
  assert sentiment_analysis_program('reviews_with_mixed_sentiments.csv') == {report output with mixed sentiments}
  assert sentiment_analysis_program('reviews_with_text_features.csv') == {report output with additional text features}
output: def sentiment_analysis_program(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import accuracy_score
  
      df = pd.read_csv(file_path)
      if df.empty:
          return {"error": "No data available for analysis"}
  
      df['review'] = df['review'].fillna('')
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(df['review'])
      y = df['sentiment']
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      report = {
          "accuracy": accuracy,
          "sentiment_distribution": df['sentiment'].value_counts().to_dict()
      }
  
      return report