parent
83fd9e0165
commit
86932401c2
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After Width: | Height: | Size: 217 KiB |
@ -1,137 +1,194 @@
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import urllib.request
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from tkinter import messagebox
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import tkinter as tk
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import tkinter.messagebox
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from PIL import Image, ImageTk
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import mysql.connector
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# 连接数据库
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connection = mysql.connector.connect(
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host="127.0.0.1",
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user="root",
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password="020425",
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database="douban"
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)
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# 使用 cursor() 方法创建一个游标对象 cursor
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cursor = connection.cursor()
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# s='三大队'
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# 创建窗口
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window = tk.Tk()
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window.title("豆瓣电影评论数据可视化")
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window.geometry("450x300")
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# welcome to image
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canvas = tk.Canvas(window, height=200, width=500)
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image_file = tk.PhotoImage(file="../image/img.png")
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image = canvas.create_image(0, 0, anchor='nw', image=image_file)
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canvas.pack(side='top')
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# 电影名
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tk.Label(window, text="电影名").place(x=100, y=230)
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# 输入框 提示
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var_search = tk.StringVar()
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var_search.set("哈尔的移动城堡")
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# 搜索的电影
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entry_search = tk.Entry(window, textvariable=var_search)
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entry_search.place(x=150,y=230)
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def search_movie():
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movie = entry_search.get()
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if movie == "":
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tk.messagebox.showwarning(title="错误", message="请输入电影名")
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# 判断数据库里面是否有该电影的URL
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else:
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data=tuple()
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# 获取输入框中的电影名
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movie = entry_search.get()
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# 使用 execute() 方法执行 SQL 查询
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cursor.execute("select url from douban01 where name=%s", (movie,))
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# 使用 fetchone() 方法获取单条数据.
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data = cursor.fetchone()
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if data != None:
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url = data[0]
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# url = 'https://movie.douban.com/review/15344422/'
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# url = urls[0]
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print(data[0])
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headers = {
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"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
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"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6",
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"Cache-Control": "max-age=0",
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"Connection": "keep-alive",
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"Cookie": 'll="118276"; bid=aRJ3WyvegZU; viewed="26979890"; ap_v=0,6.0',
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"Host": "movie.douban.com",
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"Sec-Ch-Ua": '"Microsoft Edge";v="125", "Chromium";v="125", "Not.A/Brand";v="24"',
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"Sec-Ch-Ua-Mobile": "?0",
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"Sec-Ch-Ua-Platform": '"Windows"',
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"Sec-Fetch-Dest": "document",
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"Sec-Fetch-Mode": "navigate",
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"Sec-Fetch-Site": "same-origin",
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"Sec-Fetch-User": "?1",
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"Upgrade-Insecure-Requests": "1",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0"
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}
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# (1) 请求对象的定制
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request = urllib.request.Request(url, headers=headers)
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# (2)获取响应的数据
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response = urllib.request.urlopen(request)
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content = response.read().decode('utf-8')
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# 和上面的代码一样
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with open('电影评论.html', 'w', encoding='utf-8') as fp:
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fp.write(content)
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from bs4 import BeautifulSoup
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soup = BeautifulSoup(open('电影评论.html', encoding='utf-8'), 'lxml')
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# 获取节点内容
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tags_with_class = soup.find_all(class_='review-content clearfix')
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text_list = []
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# 遍历结果集中的每个标签对象，并获取其文本内容
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for tag in tags_with_class:
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text_list.append(tag.text)
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# 将列表转换为字符串
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result_text = '\n'.join(text_list)
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# 打印文本内容
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# print(result_text)
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with open('评论.txt', 'w', encoding='utf-8') as fp:
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fp.write(result_text)
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import jieba
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import numpy
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from PIL import Image
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from wordcloud import WordCloud
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import matplotlib.pyplot as plt
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txt = open("评论.txt", "r", encoding='utf-8').read()
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words = " ".join(list(jieba.cut(txt)))
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counts = {}
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# 停用词表设置
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stopwords = [i.strip() for i in open("../Text/stopwords.txt", encoding='utf-8').readlines()]
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for word in words:
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if len(word) == 1:
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continue
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else:
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# 遍历所有词语，每出现一次其对应的值加 1
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counts[word] = counts.get(word, 0) + 1
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items = list(counts.items())
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# 根据词语出现的次数进行从大到小排序
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items.sort(key=lambda x: x[1], reverse=True)
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mask = numpy.array(Image.open("../image/bg2.png"))
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wordcloud = WordCloud(font_path='../image/SimHei.ttf',
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width=800,
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height=400,
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# mask=mask
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background_color='white',
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mode='RGBA',
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max_words=150,
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stopwords=stopwords,
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# mask=mask
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).generate(words)
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# 显示词云图
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plt.figure(figsize=(10, 5))
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plt.imshow(wordcloud, interpolation='bilinear')
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plt.title('', fontproperties='SimHei')
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plt.axis('off') # 不显示坐标轴
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# plt.show()
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# 保存词云图
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wordcloud.to_file("my_wordcloud.png")
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# tk.messagebox.showinfo(title="搜索", message="你搜索了" + movie)
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window_title = tk.Toplevel(window)
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window_title.geometry("500x300")
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window_title.title('《' + movie + '》' + "词云图")
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def connect_to_database():
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try:
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# 连接数据库
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connection = mysql.connector.connect(
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host="127.0.0.1",
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user="root",
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password="020425",
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database="douban"
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)
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cursor = connection.cursor()
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messagebox.showinfo("Success", "Connected to MySQL database successfully")
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return connection, cursor
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except Exception as e:
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messagebox.showerror("Error", f"Error connecting to MySQL database: {e}")
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url = 'https://movie.douban.com/review/1627740/'
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headers = {
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"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
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"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6",
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"Cache-Control": "max-age=0",
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"Connection": "keep-alive",
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"Cookie": 'll="118276"; bid=aRJ3WyvegZU; viewed="26979890"; ap_v=0,6.0',
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"Host": "movie.douban.com",
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"Referer": "https://movie.douban.com/subject/1308807/",
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"Sec-Ch-Ua": '"Microsoft Edge";v="125", "Chromium";v="125", "Not.A/Brand";v="24"',
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"Sec-Ch-Ua-Mobile": "?0",
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"Sec-Ch-Ua-Platform": '"Windows"',
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"Sec-Fetch-Dest": "document",
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"Sec-Fetch-Mode": "navigate",
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"Sec-Fetch-Site": "same-origin",
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"Sec-Fetch-User": "?1",
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"Upgrade-Insecure-Requests": "1",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0"
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}
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# (1) 请求对象的定制
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request = urllib.request.Request(url, headers=headers)
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# (2)获取响应的数据
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response = urllib.request.urlopen(request)
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content = response.read().decode('utf-8')
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# 和上面的代码一样
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with open('电影评论.html', 'w', encoding='utf-8') as fp:
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fp.write(content)
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from bs4 import BeautifulSoup
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soup = BeautifulSoup(open('电影评论.html',encoding='utf-8'),'lxml')
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# 获取节点内容
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tags_with_class = soup.find_all(class_='review-content clearfix')
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text_list = []
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# 遍历结果集中的每个标签对象，并获取其文本内容
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for tag in tags_with_class:
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text_list.append(tag.text)
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# 将列表转换为字符串
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result_text = '\n'.join(text_list)
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# 打印文本内容
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print(result_text)
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with open('评论.txt', 'w', encoding='utf-8') as fp:
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fp.write(result_text)
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import jieba
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import numpy
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from PIL import Image
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from wordcloud import WordCloud
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import matplotlib.pyplot as plt
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txt = open("../CommentRequest//评论.txt", "r", encoding='utf-8').read()
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words = " ".join(list(jieba.cut(txt)))
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counts = {}
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# 加载图片
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image = Image.open("my_wordcloud.png")
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image_resized = image.resize((400, 280))
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photo_image = ImageTk.PhotoImage(image=image_resized)
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# 停用词表设置
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stopwords = [i.strip() for i in open("../Text/stopwords.txt", encoding='utf-8').readlines()]
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# 创建Canvas并添加图片
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canvas = tk.Canvas(window_title, width=image_resized.width, height=image_resized.height)
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canvas.pack()
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for word in words:
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if len(word) == 1:
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continue
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else:
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# 遍历所有词语，每出现一次其对应的值加 1
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counts[word] = counts.get(word, 0) + 1
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# 在Canvas上创建一个image对象
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image_id = canvas.create_image(0, 0, anchor='nw', image=photo_image)
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items = list(counts.items())
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# 根据词语出现的次数进行从大到小排序
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items.sort(key=lambda x: x[1], reverse=True)
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# 确保图片不会被垃圾回收
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canvas.image = photo_image
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else:
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tk.messagebox.showwarning(title="错误", message="没有找到该电影")
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mask = numpy.array(Image.open("../image/bg2.png"))
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wordcloud = WordCloud(font_path='../image/SimHei.ttf',
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width=800,
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height=400,
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background_color='white',
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mode='RGBA',
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max_words=150,
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stopwords=stopwords,
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# mask=mask
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).generate(words)
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# 搜索框
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bth_search = tk.Button(window, text="搜索", command=search_movie)
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bth_search.place(x=310,y=225)
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# 显示词云图
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plt.figure(figsize=(10, 5))
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plt.imshow(wordcloud, interpolation='bilinear')
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plt.title('', fontproperties='SimHei')
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plt.axis('off') # 不显示坐标轴
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plt.show()
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# 保存词云图
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wordcloud.to_file("../image/my_wordcloud.png")
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window.mainloop()
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# 关闭数据库连接
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connection.close()
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