【getHowWordPageData.py】提取热词和相关函数定义
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from utils.getPublicData import getAllCommentsData,getAllArticleData
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from datetime import datetime
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import jieba
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from wordcloud import WordCloud
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import matplotlib.pyplot as plt
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commentsList = getAllCommentsData()
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articleList = getAllArticleData()
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def getHomeTagsData():# 统计数据库中文章个数,最高点赞作者,发布文章最多的城市
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articleLenMax = len(articleList)
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likeCountMax = 0
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likeCountMaxAuthorName = ''
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cityDic = {}
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for article in articleList:
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if likeCountMax < int(article[1]):
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likeCountMax = int(article[1])
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likeCountMaxAuthorName = article[11]
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if article[4] != '无':
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if article[4] in cityDic.keys():
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cityDic[article[4]] += 1
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else:
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cityDic[article[4]] = 1
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cityDicSorted = list(sorted(cityDic.items(),key=lambda x:x[1],reverse=True))
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return articleLenMax,likeCountMaxAuthorName,cityDicSorted[0][0]
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def getHomeCommentsLikeCountTopFore():# 获取评论中点赞最高的前四条评论
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return list(sorted(commentsList,key=lambda x:int(x[2]),reverse=True))[:4]
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def getHomeArticleCreatedAtChart():# 根据日期分别计算该日期的文章数
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xData = list(set([x[7] for x in articleList]))
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xData = list(sorted(xData,key=lambda x:datetime.strptime(x,'%Y-%m-%d').timestamp(),reverse=True))
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yData = [0 for x in range(len(xData))]
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for article in articleList:
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for index,j in enumerate(xData):# 返回索引和值
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if article[7] == j:
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yData[index] += 1
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return xData,yData
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def getHomeTypeChart():# 统计每种类型的文章数量
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typeDic = {}
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for article in articleList:
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if article[8] in typeDic.keys():
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typeDic[article[8]] += 1
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else:
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typeDic[article[8]] = 1
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resultData = []
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for key,value in typeDic.items():
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resultData.append({
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'name':key,
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'value':value
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})
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return resultData
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def getHomeCommentCreatedChart():# 统计每天用户评论数量
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createAtDic = {}
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for comment in commentsList:
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if comment[1] in createAtDic.keys():
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createAtDic[comment[1]] += 1
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else:
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createAtDic[comment[1]] = 1
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resultData = []
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for key, value in createAtDic.items():
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resultData.append({
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'name': key,
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'value': value
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})
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return resultData
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def stopWordList():
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return [line.strip() for line in open('./stopWords.txt',encoding='utf8').readlines()]
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def getUserNameWordCloud():# 生成用户名字词云
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text = ''
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stopWords = stopWordList()
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for comment in commentsList:
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text += comment[5]
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cut = jieba.cut(text)
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newCut = []
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for word in cut:
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if word not in stopWords:newCut.append(word)
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string = ' '.join(newCut)
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wc = WordCloud(
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width=1000,
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height=600,
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background_color='#fff',
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colormap='Blues',
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font_path='STHUPO.TTF'
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)
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wc.generate_from_text(string)
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fig = plt.figure(1)
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plt.imshow(wc)
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plt.axis('off')
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plt.savefig('./static/authorNameCloud.jpg',dpi=500)
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from utils.getPublicData import *
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def getHotWordLen(hotWord):# 统计包含特定热词评论数量
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commentsList = getAllCommentsData()
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hotWordLen = 0
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for i in commentsList:
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if i[4].find(hotWord) != -1:
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hotWordLen+=1
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return hotWordLen
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def getHotWordPageCreatedAtCharData(hotWord):# 统计包含特定热词的评论在每个日期的数量,并返回日期和对应的评论数量
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commentsList = getAllCommentsData()
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createdAt = {}
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for i in commentsList:
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if i[4].find(hotWord) != -1:
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if i[1] in createdAt.keys():
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createdAt[i[1]] += 1
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else:
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createdAt[i[1]] = 1
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return list(createdAt.keys()),list(createdAt.values())
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def getCommentFilterData(hotWord):# 筛选包含特定热词的评论并返回这些评论的数据
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commentsList = getAllCommentsData()
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commentData = []
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for i in commentsList:
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if i[4].find(hotWord) != -1:
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commentData.append(i)
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return commentData
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+17
-1
@@ -1,4 +1,7 @@
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from utils.query import query
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import re
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import sys
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import pandas as pd
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sys.path.append('model')
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cityList = [
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@@ -72,4 +75,17 @@ def getAllCommentsData():
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def getAllArticleData():
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articleList = query('select * from article',[],'select')
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return articleList
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return articleList
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def getAllHotWords():
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data = []
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df = pd.read_csv('./model/cipingTotal.csv',encoding='utf8')
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for i in df.values:
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try:
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data.append([
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re.search('[\u4e00-\u9fa5]+',str(i)).group(),
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re.search('\d+',str(i)).group()
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])
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except:
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continue
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return data
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