python微博舆情分析代码

以下是一个简单的Python微博舆情分析代码示例：

1. 安装所需的库

!pip install snownlp
!pip install jieba
!pip install wordcloud
!pip install matplotlib
!pip install seaborn
!pip install pandas

2. 导入所需的库

import re
import jieba
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from snownlp import SnowNLP
from wordcloud import WordCloud

3. 定义函数

# 清洗文本
def clean_text(text):
    text = re.sub(r'[^\u4e00-\u9fa5]+', '', text)
    return text

# 分词
def seg_words(text):
    words = jieba.cut(text)
    return ' '.join(words)

# 情感分析
def calc_sentiment(text):
    s = SnowNLP(text)
    sentiment = s.sentiments
    return sentiment

# 生成词云
def gen_wordcloud(text):
    wc = WordCloud(background_color='white', max_words=50, font_path='simhei.ttf', width=800, height=600)
    wc.generate(text)
    plt.imshow(wc)
    plt.axis('off')
    plt.show()

4. 读取微博数据

# 读取数据
data = pd.read_csv('weibo.csv', encoding='utf-8')

# 数据预处理
data['content'] = data['content'].apply(clean_text)
data['content'] = data['content'].apply(seg_words)
data['sentiment'] = data['content'].apply(calc_sentiment)

5. 分析数据

# 绘制情感分析的直方图
sns.histplot(data=data, x='sentiment', kde=True)
plt.show()

# 生成词云
text = ' '.join(data['content'].tolist())
gen_wordcloud(text)

以上是一个简单的微博舆情分析代码示例。可以根据具体需求对代码进行修改和扩展。