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1、项目介绍

技术栈：
Python语言、Django框架、MySQL数据库、Echarts可视化、情感分析、HTML

商品评论数据分析可视化系统是基于Python语言和Django框架开发的一个Web应用程序。它的主要功能是对商品评论数据进行分析，并将分析结果通过Echarts可视化库展示出来。

系统的数据存储使用MySQL数据库，用户可以将商品评论数据导入到数据库中。系统会对评论数据进行情感分析，判断评论的情感倾向（如积极、消极或中性）。然后，系统会根据不同的维度（如时间、地域、商品类型等）对评论数据进行统计和分析。

2、项目界面

（1）评论数据分析

（2）评论数据情感分析

（3）评论数据

（4）评论数据聚类分析

（5）注册登录界面

3、项目说明

商品评论数据分析可视化系统是基于Python语言和Django框架开发的一个Web应用程序。它的主要功能是对商品评论数据进行分析，并将分析结果通过Echarts可视化库展示出来。

系统的数据存储使用MySQL数据库，用户可以将商品评论数据导入到数据库中。系统会对评论数据进行情感分析，判断评论的情感倾向（如积极、消极或中性）。然后，系统会根据不同的维度（如时间、地域、商品类型等）对评论数据进行统计和分析。

用户可以通过系统的界面选择不同的分析维度和时间范围，系统会根据用户的选择生成相应的图表和报表。这些图表可以直观地展示出评论数据的情况，帮助用户了解消费者对商品的评价、产品的销售趋势等信息。

系统的界面使用HTML编写，可以在Web浏览器中访问。用户可以通过系统提供的登录和注册功能进行身份验证，并可以根据自己的权限进行操作。系统还提供了导出数据和分享图表的功能，方便用户将分析结果保存或分享给其他人。

总之，商品评论数据分析可视化系统能够帮助用户更好地了解商品评论数据，发现潜在的问题和机会，并支持用户做出相应的决策。

4、核心代码

import os
import re

import jieba
import pandas as pd
from django.http import JsonResponse
from django.shortcuts import render, redirect
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from tqdm import tqdm

from app.models import *


# 验证登录
def check_login(func):
    def wrapper(request):
        # print("装饰器验证登录")
        cookie = request.COOKIES.get('uid')
        if not cookie:
            return redirect('/login/')
        else:
            return func(request)
    return wrapper


# 加载数据到数据库中  并且对数据进行一定的清洗
def data2mysql(request):
    # 需要把之前的文件全部删除了
    WeiBo.objects.all().delete()
    raw_json = pd.read_csv(os.path.join('data','%23新冠肺炎%23.csv'))
    for i in tqdm(range(len(raw_json))):
        content = raw_json.iloc[i]['商品评论']
        img = raw_json.iloc[i]['商品评论图片url'] # 有空值
        img = str(img)
        if img != 'nan':
            img = eval(img)[0]

        # continue
        time = raw_json.iloc[i]['评论时间']
        shuxing = raw_json.iloc[i]['商品属性']
        other = raw_json.iloc[i]['其他数']
        pingfen = raw_json.iloc[i]['评论数']
        count = raw_json.iloc[i]['图片总数']
        url = raw_json.iloc[i]['url'] # 有空值



        # bid = raw_json.iloc[i]['bid']
        # userid = raw_json.iloc[i]['user_id']
        name = raw_json.iloc[i]['用户昵称']
        fenlei = raw_json.iloc[i]['发布位置'] # 有空值
        topic = raw_json.iloc[i]['话题']
        if not WeiBo.objects.filter(content=content,img=img,time=time,shuxing=shuxing,
                                    other=other,pingfen=pingfen,count=count,url=url,
                                    name=name,fenlei=fenlei,topic=topic):
            WeiBo.objects.create(content=content, img=img, time=time, shuxing=shuxing,
                                 other=other, pingfen=pingfen, count=count, url=url,
                                 name=name, fenlei=fenlei, topic=topic)
    return JsonResponse({'status':1,'msg':'操作成功'} )


###首页
@check_login
def index(request):
    # 话题列表
    topic_raw = [item.topic for item in WeiBo.objects.all() if item.topic]
    topic_list = []
    for item in topic_raw:
        topic_list.extend(item.split(','))
    topic_list = list(set(topic_list))
    # yon用户信息
    uid = int(request.COOKIES.get('uid', -1))
    if uid != -1:
        username = User.objects.filter(id=uid)[0].name
    # 得到话题
    if 'key' not in request.GET:
        key = topic_list[0]
        raw_data = WeiBo.objects.all()
    else:
        key= request.GET.get('key')
        raw_data = WeiBo.objects.filter(topic__contains=key)
    # 分页
    if 'page' not in request.GET:
        page = 1
    else:
        page = int(request.GET.get('page'))
    data_list = raw_data[(page-1)*20 : page*20     ]
    return render(request, 'index.html', locals())
# 情感分类
def fenlei(request):
    from snownlp import SnowNLP
    # j = '我喜欢你'
    # s = SnowNLP(j)
    # print(s.sentiments)

    for item in tqdm(WeiBo.objects.all()):
        emotion = '正向' if SnowNLP(item.content).sentiments >0.45 else '负向'
        WeiBo.objects.filter(id=item.id).update(emotion=emotion)
    return JsonResponse({'status':1,'msg':'操作成功'} )



# 登录
def login(request):
    if request.method == "POST":
        tel, pwd = request.POST.get('tel'), request.POST.get('pwd')
        if User.objects.filter(tel=tel, password=pwd):

            obj = redirect('/')
            obj.set_cookie('uid', User.objects.filter(tel=tel, password=pwd)[0].id, max_age=60 * 60 * 24)
            return obj
        else:
            msg = "用户信息错误，请重新输入！！"
            return render(request, 'login.html', locals())
    else:
        return render(request, 'login.html', locals())

# 注册
def register(request):
    if request.method == "POST":
        name, tel, pwd = request.POST.get('name'), request.POST.get('tel'), request.POST.get('pwd')
        print(name, tel, pwd)
        if User.objects.filter(tel=tel):
            msg = "你已经有账号了，请登录"
        else:
            User.objects.create(name=name, tel=tel, password=pwd)
            msg = "注册成功，请登录！"
        return render(request, 'login.html', locals())
    else:
        msg = ""
        return render(request, 'register.html', locals())

# 注销
def logout(request):
    obj = redirect('index')
    obj.delete_cookie('uid')
    return obj

# 商品评论可视化
@check_login
def plot(request):
    """
    折线图   每月发表数
    柱状图   每日发表商品评论前20
    饼图  正负向
    柱状图  评论前十
    """
    uid = int(request.COOKIES.get('uid', -1))
    if uid != -1:
        username = User.objects.filter(id=uid)[0].name
    #1 折线图   每天发布商品评论折线图
    raw_data = WeiBo.objects.all()
    main1 = [item.time.strftime('%Y-%m-%d') for item in raw_data]
    main1_x = sorted(list(set(main1)))
    main1_y = [main1.count(item) for item in main1_x]



    #2 柱状图   发表商品评论前20 日期
    raw_data = WeiBo.objects.all()
    main2 = [item.time.strftime('%Y-%m-%d') for item in raw_data]
    main2set = sorted(list(set(main2)))
    main2_x = {item:main2.count(item)  for item in main2set}
    main2 = sorted(main2_x.items(),key=lambda x:x[1],reverse=True)[:20]
    print(main2)
    main2_x = [item[0] for item in main2]
    main2_y = [item[1] for item in main2]

    #3饼图
    main3 = [item.emotion+'情感' for item in raw_data]
    main3_y = {}
    for item in main3:
        main3_y[item] = main3_y.get(item,0) + 1
    main3 = [{
        'value':v,
        'name':k
    } for k,v in main3_y.items() ]



    #4柱状图
    raw_data = raw_data.order_by('-pingfen')[:10]
    main4_x = [f'id={itme.id}' for itme in raw_data]
    main4_y = [itme.pingfen for itme in raw_data]



    return render(request,'plot.html',locals())


####情感分类可视化
@check_login
def qingganPlot(request):
    """
    折线图   每月发表数
    柱状图   每日发表商品评论前20
    饼图  正负向
    柱状图  评论前十
    """
    uid = int(request.COOKIES.get('uid', -1))
    if uid != -1:
        username = User.objects.filter(id=uid)[0].name
    #1 折线图   每天发布商品评论折线图
    raw_data = WeiBo.objects.all()
    main1 = [item.time.strftime('%Y-%m-%d') for item in raw_data]
    main1_x = sorted(list(set(main1)))
    main1_y1 = []
    for item in main1_x:
        year = int(item.split('-')[0])
        month = int(item.split('-')[1])
        day = int(item.split('-')[2])
        main1_y1.append(raw_data.filter(emotion='正向',time__year=year,time__month=month,time__day=day).count())

    main1_y2 = []
    for item in main1_x:
        year = int(item.split('-')[0])
        month = int(item.split('-')[1])
        day = int(item.split('-')[2])
        main1_y2.append(raw_data.filter(emotion='负向', time__year=year, time__month=month, time__day=day).count())

    main1_data = ['正向','负向']
    main1_y = [
        {
            'name': '正向',
            'type': 'line',
            'data': main1_y1
        },
        {
            'name': '负向',
            'type': 'line',
            'data': main1_y2
        },

    ]



    #2 柱状图   发表商品评论前20riqi 日期
    stop = [item.strip()      for item in open(os.path.join('stopwords','hit_stopwords.txt')  , 'r',encoding='UTF-8').readlines()]
    stop.extend([item.strip() for item in open(os.path.join('stopwords','scu_stopwords.txt'  ), 'r',encoding='UTF-8').readlines()])
    stop.extend([item.strip() for item in open(os.path.join('stopwords','baidu_stopwords.txt'), 'r',encoding='UTF-8').readlines()])
    stop.extend([item.strip() for item in open(os.path.join('stopwords','cn_stopwords.txt'   ), 'r',encoding='UTF-8').readlines()])

    main5_data = WeiBo.objects.filter(emotion='正向')[:1000]
    main5_json = {}
    for item in main5_data:
        text1 = list(jieba.cut(item.content.replace('#','').replace('O','').replace('L','').replace('.','')))
        for t in text1:
            if t in stop or t.strip() == '':
                continue
            if t not in main5_json.keys():
                main5_json[t] = 1
            else:
                main5_json[t] += 1
    result_dict = sorted(main5_json.items(), key=lambda x: x[1], reverse=True)[:20]  # 最大到最小
    main2_x = [item[0]  for item in result_dict]
    main2_y = [item[1]  for item in result_dict]

    #3饼图
    main3 = [item.emotion+'情感' for item in raw_data]
    main3_y = {}
    for item in main3:
        main3_y[item] = main3_y.get(item,0) + 1
    main3 = [{
        'value':v,
        'name':k
    } for k,v in main3_y.items() ]




    ## 5
    stop = [item.strip() for item in open(os.path.join('stopwords', 'hit_stopwords.txt'), 'r',encoding='UTF-8').readlines()]
    stop.extend([item.strip() for item in open(os.path.join('stopwords', 'scu_stopwords.txt'), 'r',encoding='UTF-8').readlines()])
    stop.extend([item.strip() for item in open(os.path.join('stopwords', 'baidu_stopwords.txt'), 'r',encoding='UTF-8').readlines()])
    stop.extend([item.strip() for item in open(os.path.join('stopwords', 'cn_stopwords.txt'), 'r',encoding='UTF-8').readlines()])

    main5_data = WeiBo.objects.filter(emotion='正向')[:1000]
    main5_json = {}
    for item in main5_data:
        text1 = list(jieba.cut(item.content))
        for t in text1:
            if t in stop or t.strip() == '':
                continue
            if t not in main5_json.keys():
                main5_json[t] = 1
            else:
                main5_json[t] += 1
    result_dict = sorted(main5_json.items(), key=lambda x: x[1], reverse=True)[:30]  # 最大到最小
    # print(result_dict)
    main5_data = [{
        "name": item[0],
        "value": item[1]
    } for item in result_dict]
    # 6
    main6_data = WeiBo.objects.filter(emotion='负向')[:1000]
    main6_json = {}
    for item in main6_data:
        text1 = list(jieba.cut(item.content))
        for t in text1:
            if t in stop or t.strip() == '':
                continue
            if t not in main6_json.keys():
                main6_json[t] = 1
            else:
                main6_json[t] += 1
    result_dict = sorted(main6_json.items(), key=lambda x: x[1], reverse=True)[:30]  # 最大到最小
    # print(result_dict)
    main6_data = [{
        "name": item[0],
        "value": item[1]
    } for item in result_dict]
    ########7话题词云图
    topic_raw = [item.topic for item in WeiBo.objects.all() if item.topic]
    topic_list = []
    for item in topic_raw:
        topic_list.extend(item.split(','))
    topic_set = list(set(topic_list))
    main7_data = [{
        "name": item,
        "value": topic_list.count(item)
    } for item in topic_set]

    main7_data = sorted(main7_data,key=lambda  x:x['value'],reverse=True)[:10]
    return render(request,'qingganPlot.html',locals())

# 个人中心
@check_login
def my(request):
    uid = int(request.COOKIES.get('uid', -1))
    if uid != -1:
        username = User.objects.filter(id=uid)[0].name
    if request.method == "POST":
        name,tel,password = request.POST.get('name'),request.POST.get('tel'),request.POST.get('password1')
        User.objects.filter(id=uid).update(name=name,tel=tel,password=password)
        return redirect('/')
    else:
        my_info = User.objects.filter(id=uid)[0]
        return render(request,'my.html',locals())


# 清洗文本
def clearTxt(line:str):
    if(line != ''):
        line = line.strip()
        # 去除文本中的英文和数字
        line = re.sub("[a-zA-Z0-9]", "", line)
        # 去除文本中的中文符号和英文符号
        line = re.sub("[\s+\.\!\/_,$%^*(+\"\'；：“”．]+|[+——！，。？?、~@#￥%……&*（）]+", "", line)
        return line
    return None

#文本切割
def sent2word(line):
    segList = jieba.cut(line,cut_all=False)
    segSentence = ''
    for word in segList:
        if word != '\t':
            segSentence += word + " "
    return segSentence.strip()
def  kmeansPlot(request):
    uid = int(request.COOKIES.get('uid', -1))
    if uid != -1:
        username = User.objects.filter(id=uid)[0].name

    # 聚类个数
    if 'num' in request.GET:
        num = int(request.GET.get('num'))
    else:
        num = 2
    ### 训练
    # 清洗文本
    clean_data = [item.content for item in WeiBo.objects.all()]
    clean_data = [clearTxt(item) for item in clean_data]
    clean_data = [sent2word(item) for item in clean_data]

    # 该类会将文本中的词语转换为词频矩阵，矩阵元素a[i][j] 表示j词在i类文本下的词频
    vectorizer = CountVectorizer(max_features=20000)
    # 该类会统计每个词语的tf-idf权值
    tf_idf_transformer = TfidfTransformer()
    # 将文本转为词频矩阵并计算tf-idf
    tfidf = tf_idf_transformer.fit_transform(vectorizer.fit_transform(clean_data))
    # 获取词袋模型中的所有词语
    tfidf_matrix = tfidf.toarray()
    # 获取词袋模型中的所有词语
    word = vectorizer.get_feature_names()

    # 聚成5类
    from sklearn.cluster import KMeans
    clf = KMeans(n_clusters=num)
    result_list = clf.fit(tfidf_matrix)
    result_list  = list(clf.predict(tfidf_matrix))


    #####k可视化处理
    ## 1 饼图
    """{
            value: 735,
            name: 'Direct'
        }
    """
    pie_data = [
        {
            'value': result_list.count(i),
            'name': f'第{i+1}类'
        }
        for i in range(num)
    ]
    print(pie_data)


    div_id_list = [f'container{i+1}' for i in range(num)]


    data_list = []
    for label,name in enumerate(div_id_list):
        tmp = {'id':name,'data':[],'title':f'第{label+1}类'}
        # 汇总
        tmp_text_list = ''
        for la,text in zip(result_list,clean_data):
            if la == label:
                tmp_text_list += ' ' + text
        tmp_text_list = [item for item in tmp_text_list.split(' ') if item.strip() != ' ']

        # 得到前30
        rank_Data = [
            {
                'value': tmp_text_list.count(item),
                'name': item
            }
            for item in set(tmp_text_list)
        ]
        rank_Data = sorted(rank_Data,key=lambda  x: x['value'],reverse=True)[:100]


        tmp['data'] =  rank_Data
        data_list.append(tmp)

    return render(request, 'kmeansPlot.html', locals())

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