Textblob Text Classification Practice

To implement TextBlob text classification in practice, it is first necessary to build a Python environment and install class libraries such as TextBlob, NLTK, and scikit learn. 1. Environmental construction: -Install Python: Download and install the latest version of Python from the Python official website. -Install TextBlob: Run the following command from the command line to install TextBlob: pip install textblob -Install NLTK: Run the following command from the command line to install NLTK: pip install nltk -Install scikit learn: Run the following command from the command line to install scikit learn: pip install -U scikit-learn 2. Dependent class libraries: -Textblob: Used for tasks such as text processing, sentiment analysis, and text classification. -NLTK: used for Natural language processing tasks, such as tokenization, part of speech tagging, etc. -Scikit learn: used for machine learning tasks, including classification, clustering, etc. 3. Dataset introduction and download: -A commonly used text classification dataset is 20 Newsgroups, which contains 20 news documents with different themes. You can download the dataset from the following website: https://archive.ics.uci.edu/ml/datasets/Twenty +Newsgroups 4. Sample data description: This sample uses the 20 Newsgroups dataset, divided into 20 different categories. Each category has multiple news documents, and we need to categorize these documents. 5. Complete sample code: python import nltk from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score #Download and load the 20 Newsgroups dataset newsgroups_train = fetch_20newsgroups(subset='train') #Using NLTK for text processing nltk.download('punkt') #Define Text Extractor tfidf = TfidfVectorizer() #Feature extraction and vectorization of training data X_train = tfidf.fit_transform(newsgroups_train.data) y_train = newsgroups_train.target #Training Naive Bayes classifier classifier = MultinomialNB() classifier.fit(X_train, y_train) #Predicting New Text Classifications new_doc = ['I need help with my computer'] X_new = tfidf.transform(new_doc) predicted = classifier.predict(X_new) #Print prediction results newsgroups_train.target_names[predicted[0]] #Evaluate classifier accuracy newsgroups_test = fetch_20newsgroups(subset='test') X_test = tfidf.transform(newsgroups_test.data) y_test = newsgroups_test.target y_pred = classifier.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) This sample uses the Naive Bayes classifier to classify the 20 Newsgroups dataset and output the accuracy evaluation results. Other classification algorithms and datasets can be selected according to actual needs.