Textblob Sentiment analysis

Environmental construction and preparation work: 1. Install Python and Pip: First, you need to install Python and Pip. You can download and install the corresponding version of Python from the official Python website. After the installation is completed, open a command line window and enter 'pip install textblob' to install the TextBlob library. 2. Download NLTK data: TextBlob relies on NLTK data and requires downloading some corpora and models. Enter 'Python - m textblob. download' in a command line window_ Corpora ', download the required data. Dependent class libraries: 1. TextBlob: a Python library for processing text data, which provides Sentiment analysis, text processing, Natural language processing and other functions. 2. NLTK: a Python library for Natural language processing, in which TextBlob uses some data. Datasets: TextBlob comes with some sample datasets that can be used for training and testing of sentiment analysis. The following is a complete example of conducting emotional analysis on a given sentence: python from textblob import TextBlob #Create a TextBlob object blob = TextBlob("I love this place. It's amazing.") #Obtain emotional polarity and subjectivity scores polarity = blob.sentiment.polarity subjectivity = blob.sentiment.subjectivity #Print emotional analysis results print("Polarity:", polarity) print("Subjectivity:", subjectivity) #Determine emotional polarity and output corresponding emotional classification if polarity > 0: print("Positive sentiment") elif polarity < 0: print("Negative sentiment") else: print("Neutral sentiment") Run the above code and output the following results: Polarity: 0.625 Subjectivity: 0.6 Positive sentiment Source code description: 1. First, import the TextBlob class. 2. Create a TextBlob object and pass in the text for sentiment analysis. 3. Use 'sentient. polarity' to obtain an emotional polarity score with a range of [-1, 1]. A larger value indicates a more positive emotion, while a smaller value indicates a more negative emotion. 4. Use 'sentient. subjectivity' to obtain a subjective score with a range of [0, 1]. A larger value indicates subjectivity, while a smaller value indicates objectivity. 5. Determine the emotional classification of sentences based on the positive and negative values of emotional polarity.