如何使用textblob进行英文文本情感分析，需要完成以下步骤：第一，分词并去除停用词，对分词结果进行词频统计并获取词频topn的的词；第二，提取特征词，计算特征值；第三，绘制词云图；第四，进行情感分析并分别计算正面、中性、负面的情感得分；第五，绘制情感分析占比图、直方图和波动图

。具体实现过程如下：

1. 分词并去除停用词，对分词结果进行词频统计并获取词频topn的的词

from textblob import TextBlob
import nltk
from nltk.corpus import stopwords
from collections import Counter
import matplotlib.pyplot as plt
from wordcloud import WordCloud

# 获取停用词
stop_words = set(stopwords.words('english'))

# 分词并去除停用词
def tokenize(text):
    tokens = nltk.word_tokenize(text.lower())
    return [token for token in tokens if token.isalpha() and token not in stop_words]

# 计算词频
def word_frequency(tokens, top_n=10):
    word_counts = Counter(tokens)
    top_words = word_counts.most_common(top_n)
    return dict(top_words)

# 获取文本
text = "This is a sample text for sentiment analysis. We will use TextBlob for this purpose."

# 分词并去除停用词
tokens = tokenize(text)

# 计算词频
top_words = word_frequency(tokens)

# 打印topn的词频统计结果
print(top_words)

# 绘制词云图
wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(top_words)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

2. 提取特征词，计算特征值

from textblob import TextBlob
import nltk
from nltk.corpus import stopwords
from collections import Counter
import matplotlib.pyplot as plt
from wordcloud import WordCloud

# 获取停用词
stop_words = set(stopwords.words('english'))

# 分词并去除停用词
def tokenize(text):
    tokens = nltk.word_tokenize(text.lower())
    return [token for token in tokens if token.isalpha() and token not in stop_words]

# 计算词频
def word_frequency(tokens, top_n=10):
    word_counts = Counter(tokens)
    top_words = word_counts.most_common(top_n)
    return dict(top_words)

# 提取特征词并计算特征值
def extract_features(text):
    blob = TextBlob(text)
    sentiment = blob.sentiment
    polarity = sentiment.polarity
    subjectivity = sentiment.subjectivity
    return (polarity, subjectivity)

# 获取文本
text = "This is a sample text for sentiment analysis. We will use TextBlob for this purpose."

# 分词并去除停用词
tokens = tokenize(text)

# 计算词频
top_words = word_frequency(tokens)

# 打印topn的词频统计结果
print(top_words)

# 绘制词云图
wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(top_words)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

# 提取特征词并计算特征值
polarity, subjectivity = extract_features(text)

# 打印特征值
print(f'Polarity: {polarity:.2f}')
print(f'Subjectivity: {subjectivity:.2f}')

3. 进行情感分析并分别计算正面、中性、负面的情感得分

from textblob import TextBlob
import nltk
from nltk.corpus import stopwords
from collections import Counter
import matplotlib.pyplot as plt
from wordcloud import WordCloud

# 获取停用词
stop_words = set(stopwords.words('english'))

# 分词并去除停用词
def tokenize(text):
    tokens = nltk.word_tokenize(text.lower())
    return [token for token in tokens if token.isalpha() and token not in stop_words]

# 计算词频
def word_frequency(tokens, top_n=10):
    word_counts = Counter(tokens)
    top_words = word_counts.most_common(top_n)
    return dict(top_words)

# 提取特征词并计算特征值
def extract_features(text):
    blob = TextBlob(text)
    sentiment = blob.sentiment
    polarity = sentiment.polarity
    subjectivity = sentiment.subjectivity
    return (polarity, subjectivity)

# 进行情感分析并计算情感得分
def analyze_sentiment(text):
    blob = TextBlob(text)
    sentiment = blob.sentiment
    polarity = sentiment.polarity
    if polarity > 0:
        return 'positive'
    elif polarity < 0:
        return 'negative'
    else:
        return 'neutral'

# 获取文本
text = "This is a sample text for sentiment analysis. We will use TextBlob for this purpose."

# 分词并去除停用词
tokens = tokenize(text)

# 计算词频
top_words = word_frequency(tokens)

# 打印topn的词频统计结果
print(top_words)

# 绘制词云图
wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(top_words)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

# 提取特征词并计算特征值
polarity, subjectivity = extract_features(text)

# 打印特征值
print(f'Polarity: {polarity:.2f}')
print(f'Subjectivity: {subjectivity:.2f}')

# 进行情感分析并计算情感得分
sentiment = analyze_sentiment(text)

# 打印情感分析结果
print(f'Sentiment: {sentiment}')

4. 绘制情感分析占比图、直方图和波动图

from textblob import TextBlob
import nltk
from nltk.corpus import stopwords
from collections import Counter
import matplotlib.pyplot as plt
from wordcloud import WordCloud

# 获取停用词
stop_words = set(stopwords.words('english'))

# 分词并去除停用词
def tokenize(text):
    tokens = nltk.word_tokenize(text.lower())
    return [token for token in tokens if token.isalpha() and token not in stop_words]

# 计算词频
def word_frequency(tokens, top_n=10):
    word_counts = Counter(tokens)
    top_words = word_counts.most_common(top_n)
    return dict(top_words)

# 提取特征词并计算特征值
def extract_features(text):
    blob = TextBlob(text)
    sentiment = blob.sentiment
    polarity = sentiment.polarity
    subjectivity = sentiment.subjectivity
    return (polarity, subjectivity)

# 进行情感分析并计算情感得分
def analyze_sentiment(text):
    blob = TextBlob(text)
    sentiment = blob.sentiment
    polarity = sentiment.polarity
    if polarity > 0:
        return 'positive'
    elif polarity < 0:
        return 'negative'
    else:
        return 'neutral'

# 获取文本
text = "This is a sample text for sentiment analysis. We will use TextBlob for this purpose."

# 分词并去除停用词
tokens = tokenize(text)

# 计算词频
top_words = word_frequency(tokens)

# 打印topn的词频统计结果
print(top_words)

# 绘制词云图
wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(top_words)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

# 提取特征词并计算特征值
polarity, subjectivity = extract_features(text)

# 打印特征值
print(f'Polarity: {polarity:.2f}')
print(f'Subjectivity: {subjectivity:.2f}')

# 进行情感分析并计算情感得分
sentiment = analyze_sentiment(text)

# 打印情感分析结果
print(f'Sentiment: {sentiment}')

# 绘制情感分析占比图
labels = ['Positive', 'Neutral', 'Negative']
sizes = [0, 0, 0]
if sentiment == 'positive':
    sizes[0] = 1
elif sentiment == 'neutral':
    sizes[1] = 1
else:
    sizes[2] = 1
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)
plt.axis('equal')
plt.show()

# 绘制情感得分直方图
scores = [polarity]
plt.hist(scores, bins=10)
plt.xlabel('Polarity')
plt.ylabel('Frequency')
plt.title('Sentiment Analysis')
plt.show()

# 绘制情感得分波动图
plt.plot(scores)
plt.xlabel('Sentence')
plt.ylabel('Polarity')
plt.title('Sentiment Analysis')
plt.show()