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[免费]【NLP舆情分析】基于python微博舆情分析可视化系统(flask+pandas+echarts)【论文+源码+SQL脚本】

news 2025/7/29 5:06:11

大家好，我是python222_小锋老师，看到一个不错的【NLP舆情分析】基于python微博舆情分析可视化系统(flask+pandas+echarts)，分享下哈。

项目视频演示

【免费】【NLP舆情分析】基于python微博舆情分析可视化系统(flask+pandas+echarts+爬虫) Python毕业设计_哔哩哔哩_bilibili

项目介绍

随着社交媒体的快速发展，微博作为一种重要的公共信息平台，承载着大量的社会舆论和民众情感。如何有效地从微博平台获取并分析海量的舆情数据，成为了当前信息社会中的重要课题。本论文基于自然语言处理（NLP）技术，结合Flask、Pandas、ECharts和爬虫技术，设计并实现了一套微博舆情分析可视化系统。

该系统首先利用Python中的爬虫技术从微博平台实时抓取相关舆情数据，包括微博内容、评论、转发等信息。通过对抓取数据的清洗和预处理，使用Pandas进行数据分析，提取关键词、情感倾向和舆情热度等信息。接着，通过NLP技术对微博内容进行情感分析，识别出用户的情感态度（如正面、负面、中立）。最后，系统使用ECharts进行数据的可视化展示，为用户提供直观、动态的舆情分析图表，帮助决策者实时掌握舆情走向。

系统不仅能够有效地获取和分析微博上的实时舆情信息，还提供了良好的交互性和可操作性，用户可以根据需要进行舆情趋势分析、情感分布展示及关键词云等操作。通过对实际案例的分析验证，系统具有较高的准确性和实用性，能够帮助相关部门及时识别网络舆论的动态变化，并做出相应的应对措施。

本论文的研究成果为基于Python的舆情分析系统提供了一种新的实现思路，具有广泛的应用前景，尤其在公共事务管理、品牌舆情监控以及社会热点事件的监测中具有重要的实际意义。

系统展示

部分代码

import pandas as pd
from flask import Blueprint, render_template, jsonify, request
from snownlp import SnowNLPfrom dao import articleDao, commentDao
from util import wordcloudUtil, mapUtilpb = Blueprint('page', __name__, url_prefix='/page', template_folder='templates')@pb.route('/home')
def home():"""进入主页面，获取相应的数据，带到页面去:return:"""articleData = articleDao.get7DayArticle()xAxis7ArticleData = []yAxis7ArticleData = []for article in articleData:xAxis7ArticleData.append(article[0])yAxis7ArticleData.append(article[1])# 获取帖子类别数量arcTypeData = []articleTypeAmountList = articleDao.getArticleTypeAmount()for arcType in articleTypeAmountList:arcTypeData.append({'value': arcType[1], 'name': arcType[0]})# 获取top50评论用户名top50CommentUserList = commentDao.getTopCommentUser()top50CommentUserNameList = [cu[0] for cu in top50CommentUserList]str = ' '.join(top50CommentUserNameList)wordcloudUtil.genWordCloudPic(str, 'comment_mask.jpg', 'comment_user_cloud.jpg')# 获取7天评论数量commentData = []commentAmountList = commentDao.getCommentAmount()for comment in commentAmountList:commentData.append({'value': comment[1], 'name': comment[0]})return render_template('index.html',xAxis7ArticleData=xAxis7ArticleData,yAxis7ArticleData=yAxis7ArticleData,arcTypeData=arcTypeData,commentData=commentData)@pb.route('homePageData')
def getHomePageData():"""获取主页数据 ajax异步交互 前端每隔5分钟请求一次 实时数据:return:"""totalArticle = articleDao.getTotalArticle()topAuthor = articleDao.getTopAuthor()topRegion = articleDao.getTopRegion()topArticles = articleDao.getArticleTopZan()return jsonify(totalArticle=totalArticle, topAuthor=topAuthor, topRegion=topRegion, topArticles=topArticles)@pb.route('hotWord')
def hotWord():"""热词分析统计:return:"""hotwordList = []# 只读取前100条df = pd.read_csv('./fenci/comment_fre.csv', nrows=100)for value in df.values:hotwordList.append(value[0])# 获取请求参数，如果没有获取到，给个默认值 第一个列表数据defaultHotWord = request.args.get('word', default=hotwordList[0])hotwordNum = 0  # 出现次数for value in df.values:if defaultHotWord == value[0]:hotwordNum = value[1]# 情感分析sentiments = ''stc = SnowNLP(defaultHotWord).sentimentsif stc > 0.6:sentiments = '正面'elif stc < 0.2:sentiments = '负面'else:sentiments = '中性'commentHotWordData = commentDao.getCommentHotWordAmount(defaultHotWord)xAxisHotWordData = []yAxisHotWordData = []for comment in commentHotWordData:xAxisHotWordData.append(comment[0])yAxisHotWordData.append(comment[1])commentList = commentDao.getCommentByHotWord(defaultHotWord)return render_template('hotWord.html',hotwordList=hotwordList,defaultHotWord=defaultHotWord,hotwordNum=hotwordNum,sentiments=sentiments,xAxisHotWordData=xAxisHotWordData,yAxisHotWordData=yAxisHotWordData,commentList=commentList)@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)@pb.route('articleDataAnalysis')
def articleDataAnalysis():"""微博数据分析:return:"""arcTypeList = []df = pd.read_csv('./spider/arcType_data.csv')for value in df.values:arcTypeList.append(value[0])# 获取请求参数，如果没有获取到，给个默认值 第一个列表数据defaultArcType = request.args.get('arcType', default=arcTypeList[0])articleList = articleDao.getArticleByArcType(defaultArcType)xDzData = []  # 点赞x轴数据xPlData = []  # 评论x轴数据xZfData = []  # 转发x轴数据rangeNum = 1000rangeNum2 = 100for item in range(0, 10):xDzData.append(str(rangeNum * item) + '-' + str(rangeNum * (item + 1)))xPlData.append(str(rangeNum * item) + '-' + str(rangeNum * (item + 1)))for item in range(0, 20):xZfData.append(str(rangeNum2 * item) + '-' + str(rangeNum2 * (item + 1)))xDzData.append('1万+')xPlData.append('1万+')xZfData.append('2千+')yDzData = [0 for x in range(len(xDzData))]  # 点赞y轴数据yPlData = [0 for x in range(len(xPlData))]  # 评论y轴数据yZfData = [0 for x in range(len(xZfData))]  # 转发y轴数据for article in articleList:for item in range(len(xDzData)):if int(article[4]) < rangeNum * (item + 1):yDzData[item] += 1breakelif int(article[4]) > 10000:yDzData[len(xDzData) - 1] += 1breakif int(article[3]) < rangeNum * (item + 1):yPlData[item] += 1breakelif int(article[3]) > 10000:yPlData[len(xDzData) - 1] += 1breakfor article in articleList:for item in range(len(xZfData)):if int(article[2]) < rangeNum2 * (item + 1):yZfData[item] += 1breakelif int(article[2]) > 2000:yZfData[len(xZfData) - 1] += 1breakreturn render_template('articleDataAnalysis.html',arcTypeList=arcTypeList,defaultArcType=defaultArcType,xDzData=xDzData,yDzData=yDzData,xPlData=xPlData,yPlData=yPlData,xZfData=xZfData,yZfData=yZfData)@pb.route('commentDataAnalysis')
def commentDataAnalysis():"""微博评论数据分析:return:"""commentList = commentDao.getAllComment()xDzData = []  # 点赞X轴数据rangeNum = 5for item in range(0, 20):xDzData.append(str(rangeNum * item) + '-' + str(rangeNum * (item + 1)))xDzData.append('1百+')yDzData = [0 for x in range(len(xDzData))]  # 点赞y轴数据genderDic = {'男': 0, '女': 0}for comment in commentList:for item in range(len(xDzData)):if int(comment[4]) < rangeNum * (item + 1):yDzData[item] += 1breakelif int(comment[4]) > 100:yDzData[len(xDzData) - 1] += 1breakif genderDic.get(comment[8], -1) != -1:genderDic[comment[8]] += 1genderData = [{'name': x[0], 'value': x[1]} for x in genderDic.items()]# 只读取前50条数据df = pd.read_csv('./fenci/comment_fre.csv', nrows=50)hotCommentwordList = [x[0] for x in df.values]str2 = ' '.join(hotCommentwordList)wordcloudUtil.genWordCloudPic(str2, 'comment_mask.jpg', 'comment_cloud.jpg')return render_template('commentDataAnalysis.html',xDzData=xDzData,yDzData=yDzData,genderData=genderData)@pb.route('articleCloud')
def articleCloud():"""微博内容词云图:return:"""# 只读取前50条数据df = pd.read_csv('./fenci/article_fre.csv', nrows=50)hotArticlewordList = [x[0] for x in df.values]str2 = ' '.join(hotArticlewordList)wordcloudUtil.genWordCloudPic(str2, 'article_mask.jpg', 'article_cloud.jpg')return render_template('articleCloud.html')@pb.route('commentCloud')
def commentCloud():"""微博评论词云图:return:"""# 只读取前50条数据df = pd.read_csv('./fenci/comment_fre.csv', nrows=50)hotCommentwordList = [x[0] for x in df.values]str2 = ' '.join(hotCommentwordList)wordcloudUtil.genWordCloudPic(str2, 'comment_mask.jpg', 'comment_cloud.jpg')return render_template('commentCloud.html')@pb.route('commentUserCloud')
def commentUserCloud():"""微博评论用户词云图:return:"""# 获取top50评论用户名top50CommentUserList = commentDao.getTopCommentUser()top50CommentUserNameList = [cu[0] for cu in top50CommentUserList]str = ' '.join(top50CommentUserNameList)wordcloudUtil.genWordCloudPic(str, 'comment_mask.jpg', 'comment_user_cloud.jpg')return render_template('commentUserCloud.html')@pb.route('ipDataAnalysis')
def ipDataAnalysis():"""IP地址数据分析:return:"""cityDic = {}  # 微博文章作者IPcityList = mapUtil.cityListarticleList = articleDao.getAllArticle()for article in articleList:if article[5]:for city in cityList:if city['province'].find(article[5]) != -1:if cityDic.get(city['province'], -1) == -1:cityDic[city['province']] = 1else:cityDic[city['province']] += 1articleCityDicList = [{'name': x[0], 'value': x[1]} for x in cityDic.items()]cityDic2 = {}  # 微博评论作者IPcommentList = commentDao.getAllComment()for comment in commentList:if comment[3]:for city in cityList:if city['province'].find(comment[3]) != -1:if cityDic2.get(city['province'], -1) == -1:cityDic2[city['province']] = 1else:cityDic2[city['province']] += 1commentCityDicList = [{'name': x[0], 'value': x[1]} for x in cityDic2.items()]return render_template('ipDataAnalysis.html',articleCityDicList=articleCityDicList,commentCityDicList=commentCityDicList)@pb.route('sentimentAnalysis')
def sentimentAnalysis():"""舆情数据分析:return:"""xHotBarData = ['正面', '中性', '负面']yHotBarData = [0, 0, 0]# 只读取前100条df = pd.read_csv('./fenci/comment_fre.csv', nrows=100)for value in df.values:# 情感分析stc = SnowNLP(value[0]).sentimentsif stc > 0.6:yHotBarData[0] += 1elif stc < 0.2:yHotBarData[2] += 1else:yHotBarData[1] += 1hotTreeMapData = [{'name': xHotBarData[0],'value': yHotBarData[0]}, {'name': xHotBarData[1],'value': yHotBarData[1]}, {'name': xHotBarData[2],'value': yHotBarData[2]}]commentPieData = [{'name': '正面','value': 0}, {'name': '中性','value': 0}, {'name': '负面','value': 0}]articlePieData = [{'name': '正面','value': 0}, {'name': '中性','value': 0}, {'name': '负面','value': 0}]commentList = commentDao.getAllComment()for comment in commentList:# 情感分析stc = SnowNLP(comment[1]).sentimentsif stc > 0.6:commentPieData[0]['value'] += 1elif stc < 0.2:commentPieData[2]['value'] += 1else:commentPieData[1]['value'] += 1articleList = articleDao.getAllArticle()for article in articleList:# 情感分析stc = SnowNLP(article[1]).sentimentsif stc > 0.6:articlePieData[0]['value'] += 1elif stc < 0.2:articlePieData[2]['value'] += 1else:articlePieData[1]['value'] += 1df2 = pd.read_csv('./fenci/comment_fre.csv', nrows=15)xhotData15 = [x[0] for x in df2.values][::-1]yhotData15 = [x[1] for x in df2.values][::-1]return render_template('sentimentAnalysis.html',xHotBarData=xHotBarData,yHotBarData=yHotBarData,hotTreeMapData=hotTreeMapData,commentPieData=commentPieData,articlePieData=articlePieData,xhotData15=xhotData15,yhotData15=yhotData15)

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