【NLP舆情分析】基于python微博舆情分析可视化系统(flask+pandas+echarts) 视频教程 - 微博舆情分析实现
大家好，我是java1234_小锋老师，最近写了一套【NLP舆情分析】基于python微博舆情分析可视化系统(flask+pandas+echarts)视频教程，持续更新中，计划月底更新完，感谢支持。今天讲解微博舆情分析实现
视频在线地址：
2026版【NLP舆情分析】基于python微博舆情分析可视化系统(flask+pandas+echarts+爬虫) 视频教程 （火爆连载更新中..）_哔哩哔哩_bilibili
课程简介：
本课程采用主流的Python技术栈实现，Mysql8数据库，Flask后端，Pandas数据分析，前端可视化图表采用echarts，以及requests库，snowNLP进行情感分析，词频统计，包括大量的数据统计及分析技巧。
实现了，用户登录，注册，爬取微博帖子和评论信息，进行了热词统计以及舆情分析，以及基于echarts实现了数据可视化，包括微博文章分析，微博IP分析，微博评论分析，微博舆情分析。最后也基于wordcloud库实现了词云图，包括微博内容词云图，微博评论词云图，微博评论用户词云图等功能。
微博舆情分析实现
我们来实现下舆情分析功能。主要是分页显示微博数据，以及最后一列要进行情感分析。
articleData.html静态模版文件我们放到templates下：
{% extends 'base.html' %}
{% block title %}微博舆情分析{% endblock %}
{% block content %}<div class="container-fluid"><div class="row"><div class="col-lg-12"><div class="card"><div class="card-header d-flex justify-content-between"><div class="header-title"><h4 class="card-title">微博舆情分析</h4></div><div class="header-action"><i data-toggle="collapse" data-target="#datatable-1" aria-expanded="false"><svg width="20" xmlns="http://www.w3.org/2000/svg" fill="none" viewbox="0 0 24 24"stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2"d="M10 20l4-16m4 4l4 4-4 4M6 16l-4-4 4-4"></path></svg></i></div></div><div class="card-body"><div class="collapse" id="datatable-1"><div class="card"><kbd class="bg-dark"><pre id="bootstrap-datatables"class="text-white"><code>
<table id="datatable" class="table data-table table-striped table-bordered" ><thead><tr><th>Name</th><th>Position</th><th>Office</th><th>Age</th><th>Start date</th><th>Salary</th></tr></thead><tbody><tr><td>Tiger Nixon</td><td>System Architect</td><td>Edinburgh</td><td>61</td><td>2011/04/25</td><td>$320,800</td></tr><tr><td>Garrett Winters</td><td>Accountant</td><td>Tokyo</td><td>63</td><td>2011/07/25</td><td>$170,750</td></tr><tr><td>Ashton Cox</td><td>Junior Technical Author</td><td>San Francisco</td><td>66</td><td>2009/01/12</td><td>$86,000</td></tr><tr><td>Cedric Kelly</td><td>Senior Javascript Developer</td><td>Edinburgh</td><td>22</td><td>2012/03/29</td><td>$433,060</td></tr><tr><td>Airi Satou</td><td>Accountant</td><td>Tokyo</td><td>33</td><td>2008/11/28</td><td>$162,700</td></tr><tr><td>Brielle Williamson</td><td>Integration Specialist</td><td>New York</td><td>61</td><td>2012/12/02</td><td>$372,000</td></tr><tr><td>Herrod Chandler</td><td>Sales Assistant</td><td>San Francisco</td><td>59</td><td>2012/08/06</td><td>$137,500</td></tr><tr><td>Rhona Davidson</td><td>Integration Specialist</td><td>Tokyo</td><td>55</td><td>2010/10/14</td><td>$327,900</td></tr><tr><td>Colleen Hurst</td><td>Javascript Developer</td><td>San Francisco</td><td>39</td><td>2009/09/15</td><td>$205,500</td></tr><tr><td>Sonya Frost</td><td>Software Engineer</td><td>Edinburgh</td><td>23</td><td>2008/12/13</td><td>$103,600</td></tr><tr><td>Jena Gaines</td><td>Office Manager</td><td>London</td><td>30</td><td>2008/12/19</td><td>$90,560</td></tr><tr><td>Quinn Flynn</td><td>Support Lead</td><td>Edinburgh</td><td>22</td><td>2013/03/03</td><td>$342,000</td></tr><tr><td>Charde Marshall</td><td>Regional Director</td><td>San Francisco</td><td>36</td><td>2008/10/16</td><td>$470,600</td></tr><tr><td>Haley Kennedy</td><td>Senior Marketing Designer</td><td>London</td><td>43</td><td>2012/12/18</td><td>$313,500</td></tr><tr><td>Tatyana Fitzpatrick</td><td>Regional Director</td><td>London</td><td>19</td><td>2010/03/17</td><td>$385,750</td></tr><tr><td>Michael Silva</td><td>Marketing Designer</td><td>London</td><td>66</td><td>2012/11/27</td><td>$198,500</td></tr><tr><td>Paul Byrd</td><td>Chief Financial Officer (CFO)</td><td>New York</td><td>64</td><td>2010/06/09</td><td>$725,000</td></tr><tr><td>Gloria Little</td><td>Systems Administrator</td><td>New York</td><td>59</td><td>2009/04/10</td><td>$237,500</td></tr><tr><td>Bradley Greer</td><td>Software Engineer</td><td>London</td><td>41</td><td>2012/10/13</td><td>$132,000</td></tr><tr><td>Dai Rios</td><td>Personnel Lead</td><td>Edinburgh</td><td>35</td><td>2012/09/26</td><td>$217,500</td></tr>
</table>
</code></pre></kbd></div></div><div class="table-responsive"><table id="datatable-1" class="table data-table table-striped table-bordered"><thead><tr><th>文章ID</th><th>文章内容</th><th>文章作者</th><th>用户IP</th><th>类型</th><th>发布时间</th><th>转发量</th><th>评论量</th><th>点赞量</th><th>情感分析</th></tr></thead><tbody>{% for article in articleList %}<tr><td>{{ article[0] }}</td><td><a href="{{ article[8] }}" target="_blank">{{ article[1] }}</a></td><td style="width:100px"><a href="{{ article[11] }}"target="_blank">{{ article[10] }}</a></td><td style="width:100px">{{ article[5] }}</td><td style="width:50px">{{ article[7] }}</td><td class="text-center" style="width:100px">{{ article[6] }}</td><td class="text-center" style="width:60px">{{ article[2] }}💖</td><td class="text-center" style="width:60px">{{ article[3] }}👆</td><td class="text-center" style="width:60px">{{ article[4] }}👍</td><td class="text-center" style="width:80px">{% if article[-1] == '正面' %}<span class="text-success">{{ article[-1] }}</span>{% elif article[-1] == '负面' %}<span class="text-danger">{{ article[-1] }}</span>{% else %}<span class="text-info">{{ article[-1] }}</span>{% endif %}</td></tr>{% endfor %}</tbody></table></div></div></div>
</div></div></div>
{% endblock %}articleDao.py实现下获取所有微博数据：
def getAllArticle():"""获取所有帖子信息:return:"""con = Nonetry:con = dbUtil.getCon()cursor = con.cursor()sql = "select * from t_article"cursor.execute(sql)return cursor.fetchall()except Exception as e:print(e)con.rollback()return Nonefinally:dbUtil.closeCon(con)page.py里实现路由业务方法articleData：
@pb.route('/articleData')
def articleData():"""微博舆情分析:return:"""articleOldList = articleDao.getAllArticle()articleNewList = []for article in articleOldList:article = list(article)# 情感分析sentiments = ''stc = SnowNLP(article[1]).sentimentsif stc > 0.6:sentiments = '正面'elif stc < 0.2:sentiments = '负面'else:sentiments = '中性'article.append(sentiments)articleNewList.append(article)return render_template('articleData.html', articleList=articleNewList)运行显示：
