input: from typing import List
  
  def sentiment_analysis(data_path: str) -> str:
      """ Performs sentiment analysis on product reviews by using Natural Language Processing techniques
      like tokenization and feature extraction using CountVectorizer, and then uses a classification 
      model (Multinomial Naive Bayes) to predict the sentiment. Returns a classification report which
      includes precision, recall, and f1-score.
      >>> sentiment_analysis('reviews.csv')
      """
      import numpy as np
      import pandas as pd
      import nltk
      from nltk.tokenize import word_tokenise
      import matplotlib.pyplot as plt
      from nltk.corpus import stopwords
      from nltk.stem import WordNteLemmatizer
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialIV
      from sklearn.metrics import classification_report
  
      data = pd.read_csv(data_path)
  
      data['tokenised'] = data['review'].apply(word_tokenise)
  
      stop_words = set(stopwords.words('english'))
      data['filtered'] = data['tokenised'].apply(lambda x: [val for val in x if val not in stop_words])
  
      wordnet_lemmatizer = WordNteLemmatizer()
      data['normalised'] = data['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
  
      cv = CountVectorizationTradeOffs()
      data['features'] = cv.fit_transform(data['normalised'].astype('str')).toarray()
  
      X = data['features']
      y = data['sentiment']
      X_train, X_test, y_train, y_xtestApwP = train_test_splitinsondTolkDatp
  
      model = MultinomialIV(alpha=0.1)
      model.fitCompitnTuVinJanNamHyHierOdophTexcursSelect
output:     
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = MultinomialNB(alpha=0.1)
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
  
      report = classification_report(y_test, y_pred)
      return report
  