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Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Create a Python program to perform 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. ### Input: Not applicable ### Output: 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 # Read in the data data = pd.read_csv('reviews.csv') # 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)

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Hugging Face

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