Python uses NLTK to determine the emotional polarity of a paragraph of text

In order to use NLTK for emotional analysis, it is necessary to first carry out environmental construction and preparation work. The following are the steps for setting up and preparing the Python environment: 1. Install Python: Please ensure that you have already installed Python. You can download the latest stable version from the official Python website and follow the installation wizard to install it. 2. Install NLTK: After installing Python, you can use the pip package manager to install NLTK. Open a terminal or command prompt and run the following command: pip install nltk 3. Download NLTK data: NLTK provides many datasets and corpora for Natural language processing. In order to conduct affective analysis, we need to download an affective analysis corpus. Run the following code in a Python interactive environment: python import nltk nltk.download('movie_reviews') After completing the above preparation work, you can now start writing code for sentiment analysis. The following is a complete sample code for sentiment analysis using NLTK: python import nltk from nltk.corpus import movie_reviews from nltk.classify import NaiveBayesClassifier from nltk.sentiment import SentimentIntensityAnalyzer #Obtain review text and tags (emotional polarity) documents = [(list(movie_reviews.words(fileid)), category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)] #Creating feature set based on Bag-of-words model all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features = list(all_words)[:2000] def document_features(document): document_words = set(document) features = {} for word in word_features: features['contains({})'.format(word)] = (word in document_words) return features #Extract features and use them to train classifiers featuresets = [(document_features(d), c) for (d, c) in documents] train_set, test_set = featuresets[100:], featuresets[:100] classifier = NaiveBayesClassifier.train(train_set) #Using a classifier for emotional polarity judgment test_document = "This movie was horrible!" test_features = document_features(test_document.split()) sentiment = classifier.classify(test_features) #Using VADER for emotional polarity judgment analyzer = SentimentIntensityAnalyzer() vader_sentiment = analyzer.polarity_scores(test_document) print("Naive Bayes Sentiment:", sentiment) print("VADER Sentiment:", vader_sentiment['compound']) The above code is divided into the following steps: 1. Import necessary libraries: Import the required NLTK libraries, including 'nltk' and 'nltk. corpus. movie'_ Reviews', 'nltk. classification. NaiveBayesClassifier', and 'nltk. sentient. SentenceIntensityAnalyzer'. 2. Obtain data: from 'movie'_ Retrieve movie review text and emotional tags from the corpus. 3. Create a feature set: create a feature set based on the Bag-of-words model model and extract features. 4. Training classifier: use feature set to train Naive Bayes classifier. 5. Emotional classification: To test the classifier, we use a simple test text and extract features. Then use a classifier to determine emotional polarity. 6. Use VADER to determine emotional polarity: Use the VADER (Valence Aware Dictionary and sEntity Reasoner) analyzer to determine emotional polarity. 7. Print results: Print the emotional polarity judgment results of the classifier and VADER. Please note that this is only a simple example of emotional analysis and is for reference only. For more complex emotional analysis tasks, it may be necessary to use more complex models and datasets.