input: 
  from typing import List, Tuple, Dict
  import numpy as np
  import pandas as pd
  import nltk
  from nltk.tokenize import word_tokenize
  import matplotlib.pyplot as plt
  from nltk.corpus import stopwords
  from nltk.stem import WordNetLemmatizer 
  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 classification_report
  
  def sentiment_analysis(reviews_csv_path: str) -> Dict[str, List[str]]:
      """
      Perform sentiment analysis on product reviews using natural language processing techniques.
      The program tokenizes the reviews, removes stopwords, normalizes words, generates features using
      CountVectorizer, and then uses a Multinomial Naive Bayes model to classify sentiments.
  
      >>> sentiment_analysis('reviews.csv')
      {'0': [0.85, 0.10, 0.05], '1': ['The product is fantastic!', 'I hated this product.', 'Could be better.']}
      """
      # Read in the data
      data = pd.read_csv(reviews_csv_path)
  
      # Tokenise the texts
      data['tokenised'] = data['review'].apply(word_tokenize)
  
      # Remove stopwords
      stop_words = set(stopwords.words('english'))
      data['filtered'] = data['tokenised'].apply(lambda x: [val for val in x if val not in stop_words])
  
      # Normalize words
      wordnet_lemmatizer = WordNetLemmatizer()
      data['normalised'] = data['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
  
      # Generate features using CountVectorizer
      cv = CountVectorizer()
      data['features'] = cv.fit_transform(data['normalised'].astype('str')).toarray()
  
      # Split into training and test sets
      X = data['features']
      y = data['sentiment']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train model
      model = MultinomialNB(alpha=0.1)
      model.fit(X_train, y_train)
  
      # Predict using the test set
      predictions = model.predict(X_test)
  
      # Generate a classification report
      report = classification_report(y_test, predictions)
      print(report)
  
      return {
          '0': [0.85, 0.10, 0.05],
          '1': ['The product is fantastic!', 'I hated this product.', 'Could be better.']
      }
  
output:     # Calculate probabilities for each class
      probabilities = model.predict_proba(X_test)
  
      # Extract positive sentiment probabilities
      positive_probs = probabilities[:, 1]
  
      # Create a dictionary to store results
      results = {
          '0': positive_probs.tolist(),
          '1': data['review'].tolist()
      }
  
      return results