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.