├── .idea ├── .name ├── encodings.xml ├── misc.xml ├── modules.xml ├── networkPublicOpinionAnalysisSystem.iml ├── vcs.xml └── workspace.xml ├── README.md ├── data ├── roll_news_sina_com_cn.csv ├── roll_news_sina_com_cn.parquet │ ├── ._SUCCESS.crc │ ├── ._common_metadata.crc │ ├── ._metadata.crc │ ├── .part-r-00000-492f9a22-0f8b-4be1-8132-49d2ecda42af.gz.parquet.crc │ ├── .part-r-00001-492f9a22-0f8b-4be1-8132-49d2ecda42af.gz.parquet.crc │ ├── _SUCCESS │ ├── _common_metadata │ ├── _metadata │ ├── part-r-00000-492f9a22-0f8b-4be1-8132-49d2ecda42af.gz.parquet │ └── part-r-00001-492f9a22-0f8b-4be1-8132-49d2ecda42af.gz.parquet └── websites_crawled.csv ├── db.sqlite3 ├── desionTree.py ├── desionTreetest.py ├── jiebatest.py ├── learn ├── __init__.py ├── __init__.pyc ├── admin.py ├── admin.pyc ├── apps.py ├── migrations │ ├── 0001_initial.py │ ├── 0001_initial.pyc │ ├── __init__.py │ └── __init__.pyc ├── models.py ├── models.pyc ├── templates │ └── index.html ├── tests.py ├── views.py └── views.pyc ├── manage.py ├── myuntils.py ├── myuntils.pyc ├── networkPublicOpinionAnalysisSystem ├── __init__.py ├── __init__.pyc ├── settings.py ├── settings.pyc ├── urls.py ├── urls.pyc ├── wsgi.py └── wsgi.pyc ├── roll_news_sina_com_cn.csv ├── sample_libsvm_data.txt ├── select.py ├── selectTest.py ├── spider └── roll_sina_spider.js ├── stopword.txt ├── system ├── __init__.py ├── __init__.pyc ├── admin.py ├── admin.pyc ├── apps.py ├── migrations │ ├── __init__.py │ └── __init__.pyc ├── models.py ├── models.pyc ├── templates │ └── system │ │ ├── index.html │ │ ├── search.html │ │ └── showAsLabel.html ├── tests.py ├── utils.py ├── views.py └── views.pyc ├── test.py ├── tfidf.py └── 项目.txt /.idea/.name: -------------------------------------------------------------------------------- 1 | networkPublicOpinionAnalysisSystem -------------------------------------------------------------------------------- /.idea/encodings.xml: 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931 | 932 | 933 | 934 | 935 | 936 | 937 | 938 | 939 | 940 | 941 | 942 | 943 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # networkPublicOpinionAnalysisSystem 2 | 网络舆情分析系统 3 | -------------------------------------------------------------------------------- /data/roll_news_sina_com_cn.parquet/._SUCCESS.crc: -------------------------------------------------------------------------------- 1 | crc -------------------------------------------------------------------------------- /data/roll_news_sina_com_cn.parquet/._common_metadata.crc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/data/roll_news_sina_com_cn.parquet/._common_metadata.crc 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-------------------------------------------------------------------------------- /db.sqlite3: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/db.sqlite3 -------------------------------------------------------------------------------- /desionTree.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """将原始数据tfidf特征向量化后,使用desiontree""" 3 | 4 | from pyspark import SparkConf,SparkContext 5 | from pyspark.sql import SQLContext,Row,DataFrame 6 | from pyspark.ml.feature import HashingTF,IDF,Tokenizer 7 | from pyspark.ml import Pipeline 8 | from pyspark.ml.classification import DecisionTreeClassifier 9 | from pyspark.ml.feature import StringIndexer, VectorIndexer 10 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 11 | 12 | def convertDfToList(DataFrame): 13 | l = DataFrame.take(DataFrame.count()) 14 | myList = [] 15 | for row in l: 16 | dic = row.asDict() 17 | myList.append(dic) 18 | return myList 19 | 20 | def showNewsByCategory(DataFrame): 21 | """某一种类下的所有新闻,按时间排序""" 22 | temp = DataFrame.orderBy('time',ascending=1).where(DataFrame['label']==u'科技').select("*") 23 | return convertDfToList(temp) 24 | 25 | def predictLabel(label,title,model): 26 | """预测新闻的标签""" 27 | sentenceData = sqlContext.createDataFrame([ 28 | (label,title), 29 | ],['label',"title"]) 30 | tokenizer = Tokenizer(inputCol="title", outputCol="words") 31 | wordsData = tokenizer.transform(sentenceData) 32 | hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20) 33 | featurizedData = hashingTF.transform(wordsData) 34 | rescaledData = idfModel.transform(featurizedData) 35 | myprediction = model.transform(rescaledData) 36 | return myprediction 37 | 38 | 39 | """连接master""" 40 | conf = SparkConf().setAppName('tfidf').setMaster('spark://HP-Pavilion:7077') 41 | sc = SparkContext(conf=conf) 42 | sqlContext = SQLContext(sc) 43 | """处理数据集,生成特征向量""" 44 | dfTitles = sqlContext.read.parquet('roll_news_sina_com_cn.parquet') 45 | print(dfTitles.dtypes) 46 | tokenizer = Tokenizer(inputCol="title", outputCol="words") 47 | wordsData = tokenizer.transform(dfTitles) 48 | hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20) 49 | featurizedData = hashingTF.transform(wordsData) 50 | idf = IDF(inputCol="rawFeatures", outputCol="features") 51 | idfModel = idf.fit(featurizedData) 52 | rescaledData = idfModel.transform(featurizedData) 53 | rescaledData.show() 54 | for features_label in rescaledData.select("features", "rawFeatures").take(3): 55 | print(features_label) 56 | """决策树模型培训""" 57 | # Index labels, adding metadata to the label column. 58 | # Fit on whole dataset to include all labels in index. 59 | labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(rescaledData) 60 | # Automatically identify categorical features, and index them. 61 | # We specify maxCategories so features with > 4 distinct values are treated as continuous. 62 | featureIndexer =\ 63 | VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(rescaledData) 64 | 65 | # Split the data into training and test sets (30% held out for testing) 66 | (trainingData, testData) = rescaledData.randomSplit([0.7, 0.3]) 67 | 68 | # Train a DecisionTree model. 69 | dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") 70 | 71 | # Chain indexers and tree in a Pipeline 72 | pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt]) 73 | 74 | # Train model. This also runs the indexers. 75 | model = pipeline.fit(trainingData) 76 | print("---------------------------") 77 | print(type(model)) 78 | """模型测试""" 79 | # Make predictions. 80 | predictions = model.transform(testData) 81 | predictions.show() 82 | predictions.select("label","indexedLabel").show(100) 83 | # Select example rows to display. 84 | predictions.select("prediction", "indexedLabel", "features").show(5) 85 | """自主测试,单个新闻测试""" 86 | sentenceData = sqlContext.createDataFrame([ 87 | (u'科技',u"电脑 手机 集群 机器 数据 科技 云计算 大数据"), 88 | (u'体育',u"足球 篮球 冠军 成功 比赛 冠军 比分"), 89 | ],['label',"title"]) 90 | tokenizer = Tokenizer(inputCol="title", outputCol="words") 91 | wordsData = tokenizer.transform(sentenceData) 92 | hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20) 93 | featurizedData = hashingTF.transform(wordsData) 94 | rescaledData = idfModel.transform(featurizedData) 95 | myprediction = model.transform(rescaledData) 96 | print("==================================================") 97 | myprediction.show() 98 | 99 | myprediction = predictLabel(u'体育',u'足球 篮球 冠军 成功 比赛 冠军 比分',model) 100 | print(type(myprediction)) 101 | print(convertDfToList(myprediction)) 102 | 103 | """模型评估""" 104 | # Select (prediction, true label) and compute test error 105 | evaluator = MulticlassClassificationEvaluator( 106 | labelCol="indexedLabel", predictionCol="prediction", metricName="precision") 107 | accuracy = evaluator.evaluate(predictions) 108 | print("Test Error = %g " % (1.0 - accuracy)) 109 | 110 | treeModel = model.stages[2] 111 | # summary only 112 | print(treeModel) 113 | 114 | sc.stop() 115 | 116 | 117 | 118 | -------------------------------------------------------------------------------- /desionTreetest.py: -------------------------------------------------------------------------------- 1 | from pyspark import SparkContext, SQLContext,SparkConf 2 | from pyspark.ml import Pipeline 3 | from pyspark.ml.classification import DecisionTreeClassifier 4 | from pyspark.ml.feature import StringIndexer, VectorIndexer 5 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 6 | 7 | conf = SparkConf().setAppName('tfidf').setMaster('spark://HP-Pavilion:7077') 8 | sc = SparkContext(conf=conf) 9 | sqlContext = SQLContext(sc) 10 | 11 | # Load the data stored in LIBSVM format as a DataFrame. 12 | data = sqlContext.read.format("libsvm").load("sample_libsvm_data.txt") 13 | data.show() 14 | # Index labels, adding metadata to the label column. 15 | # Fit on whole dataset to include all labels in index. 16 | labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) 17 | # Automatically identify categorical features, and index them. 18 | # We specify maxCategories so features with > 4 distinct values are treated as continuous. 19 | featureIndexer =\ 20 | VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) 21 | 22 | # Split the data into training and test sets (30% held out for testing) 23 | (trainingData, testData) = data.randomSplit([0.7, 0.3]) 24 | 25 | # Train a DecisionTree model. 26 | dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") 27 | 28 | # Chain indexers and tree in a Pipeline 29 | pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt]) 30 | 31 | # Train model. This also runs the indexers. 32 | model = pipeline.fit(trainingData) 33 | 34 | # Make predictions. 35 | predictions = model.transform(testData) 36 | 37 | # Select example rows to display. 38 | predictions.select("prediction", "indexedLabel", "features").show(5) 39 | 40 | # Select (prediction, true label) and compute test error 41 | evaluator = MulticlassClassificationEvaluator( 42 | labelCol="indexedLabel", predictionCol="prediction", metricName="precision") 43 | accuracy = evaluator.evaluate(predictions) 44 | print("Test Error = %g " % (1.0 - accuracy)) 45 | 46 | treeModel = model.stages[2] 47 | # summary only 48 | print(treeModel) -------------------------------------------------------------------------------- /jiebatest.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | """将原始新闻中的title数据抽取出来就行处理,再将处理结果存储为parquet类型""" 3 | import jieba 4 | import re 5 | from pyspark import SparkConf,SparkContext 6 | from pyspark.sql import SQLContext,Row,DataFrame 7 | from string import punctuation,digits,letters,whitespace 8 | 9 | 10 | def handleAndCut(string): 11 | """字符串处理,去标点符号,中文分词,return:unicode""" 12 | #string = string.decode('utf-8') 13 | string = re.sub("[\s+\.\!\/_,$%^*(+\"\']+|[+——!:》,《”。“?、~@#¥%……&*()]+".decode("utf-8"), "".decode("utf-8"),string) 14 | string = string.encode('utf-8') 15 | string = string.translate(None,punctuation+digits+letters+whitespace) 16 | seg = jieba.cut(string) 17 | return ' '.join(seg) 18 | 19 | 20 | #seg_list = jieba.cut("我来到北京清华大学", cut_all=True) 21 | #print "Full Mode:", " ".join(seg_list) # 全模式 22 | #seg_list = jieba.cut("我来到北京清华大学", cut_all=False) 23 | #print "Default Mode:", " ".join(seg_list) # 精确模式 24 | #seg_list = jieba.cut("他来到了网易杭研大厦") # 默认是精确模式 25 | #print " ".join(seg_list) 26 | #seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所,后在日本京都大学深造") # 搜索引擎模式 27 | #print " ".join(seg_list) 28 | 29 | conf = SparkConf().setAppName('tfidf').setMaster('spark://HP-Pavilion:7077') 30 | sc = SparkContext(conf=conf) 31 | sqlContext = SQLContext(sc) 32 | 33 | rawNews = sc.textFile(name='roll_news_sina_com_cn.csv') 34 | parts = rawNews.map(lambda line:line.split(',')) 35 | #titleNews 为 unicode 36 | titleNews = parts.map(lambda p:[p[0],p[1]]) 37 | titleNewsHandled = titleNews.map(lambda line:[line[0],handleAndCut(line[1])]) 38 | temp = titleNewsHandled.map(lambda line:Row(label=line[0],title=line[1])) 39 | dfTitleNews = sqlContext.createDataFrame(temp) 40 | dfTitleNews.show() 41 | print(dfTitleNews.dtypes) 42 | dfTitleNews.write.save('roll_news_sina_com_cn.parquet') 43 | sc.stop() -------------------------------------------------------------------------------- /learn/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/learn/__init__.py -------------------------------------------------------------------------------- /learn/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/learn/__init__.pyc -------------------------------------------------------------------------------- /learn/admin.py: -------------------------------------------------------------------------------- 1 | from django.contrib import admin 2 | 3 | # Register your models here. 4 | -------------------------------------------------------------------------------- /learn/admin.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/learn/admin.pyc -------------------------------------------------------------------------------- /learn/apps.py: -------------------------------------------------------------------------------- 1 | from __future__ import unicode_literals 2 | 3 | from django.apps import AppConfig 4 | 5 | 6 | class LearnConfig(AppConfig): 7 | name = 'learn' 8 | -------------------------------------------------------------------------------- /learn/migrations/0001_initial.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Generated by Django 1.9.1 on 2016-01-31 03:18 3 | from __future__ import unicode_literals 4 | 5 | from django.db import migrations, models 6 | 7 | 8 | class Migration(migrations.Migration): 9 | 10 | initial = True 11 | 12 | dependencies = [ 13 | ] 14 | 15 | operations = [ 16 | migrations.CreateModel( 17 | name='Person', 18 | fields=[ 19 | ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), 20 | ('name', models.CharField(max_length=30)), 21 | ('age', models.IntegerField()), 22 | ], 23 | ), 24 | ] 25 | -------------------------------------------------------------------------------- /learn/migrations/0001_initial.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/learn/migrations/0001_initial.pyc -------------------------------------------------------------------------------- /learn/migrations/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/learn/migrations/__init__.py -------------------------------------------------------------------------------- /learn/migrations/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/learn/migrations/__init__.pyc -------------------------------------------------------------------------------- /learn/models.py: -------------------------------------------------------------------------------- 1 | from __future__ import unicode_literals 2 | 3 | from django.db import models 4 | 5 | # Create your models here. 6 | class Person(models.Model): 7 | name = models.CharField(max_length=30) 8 | age = models.IntegerField() -------------------------------------------------------------------------------- /learn/models.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/learn/models.pyc -------------------------------------------------------------------------------- /learn/templates/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 自学django 5 | 6 | 7 |

django 基础用法

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10 | {% for item in list %} 11 | {{ item }} 12 | {% endfor %} 13 |

for 循环 判断 字典

14 | {% for i,j in dict.items %} 15 | {{ i }}:{{ j }} 16 | {% endfor %} 17 | 18 | -------------------------------------------------------------------------------- /learn/tests.py: -------------------------------------------------------------------------------- 1 | from django.test import TestCase 2 | 3 | # Create your tests here. 4 | -------------------------------------------------------------------------------- /learn/views.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | from django.shortcuts import render 3 | from django.http import HttpResponse 4 | from pyspark import SparkConf,SparkContext 5 | from pyspark.sql import SQLContext,Row 6 | import csv 7 | import StringIO 8 | from networkPublicOpinionAnalysisSystem import settings 9 | 10 | # Create your views here. 11 | 12 | def index(request): 13 | string = u'template显示字符串变量' 14 | list = ['第一','第二','第三'] 15 | tuple = ('q','w','e','r','t') 16 | dict = {'a':1,'b':2,'c':3,'d':4} 17 | conf = SparkConf().setAppName("djangotest").setMaster("spark://HP-Pavilion:7077") 18 | sc = SparkContext(conf=conf) 19 | sqlContext = SQLContext(sc) 20 | url='jdbc:mysql://127.0.0.1:3306?user=root&password=raymon' 21 | dbtable='networkPublicOpinionAnalysisSystem.test' 22 | df = sqlContext.read.format('jdbc').options(url=url,dbtable=dbtable).load() 23 | lines = sc.textFile(settings.BASE_DIR+'/system/data/roll_news_sina_com_cn.csv') 24 | parts = lines.map(lambda l:l.split(',')) 25 | schemaNews = parts.map(lambda p : Row(category=p[0],title=p[1],url=p[2],time=p[3])) 26 | news = sqlContext.createDataFrame(schemaNews) 27 | # news.registerTempTable('test') 28 | # dbtable = 'networkPublicOpinionAnalysisSystem.test' 29 | # news.write.format('jdbc').options(url=url).insertInto(tableName=dbtable) 30 | # string = news.count() 31 | row = news.first() 32 | a = Row() 33 | print(type(news)) 34 | print(type(row)) 35 | # print(type(a)) 36 | # dict = row.asDict() 37 | # string = dict['title'] 38 | 39 | # news.write.jdbc(url,table=dbtable) 40 | return render(request,'index.html',{'string':string,'list':list,'tuple':tuple,'dict':dict}) 41 | 42 | def loadRecord(line): 43 | input = StringIO.StringIO(line) 44 | reader = csv.DictReader(input, fieldnames=['category_id','title','url','time']) 45 | return reader.next() 46 | def test(request): 47 | return HttpResponse(u'使用HttpResponse传递变量') 48 | -------------------------------------------------------------------------------- /learn/views.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/learn/views.pyc -------------------------------------------------------------------------------- /manage.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import os 3 | import sys 4 | 5 | if __name__ == "__main__": 6 | os.environ.setdefault("DJANGO_SETTINGS_MODULE", "networkPublicOpinionAnalysisSystem.settings") 7 | 8 | from django.core.management import execute_from_command_line 9 | 10 | execute_from_command_line(sys.argv) 11 | -------------------------------------------------------------------------------- /myuntils.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | from string import punctuation,digits,letters,whitespace 3 | import jieba 4 | import re 5 | 6 | def handleAndCut(string): 7 | """字符串处理,去标点符号,中文分词""" 8 | #string = string.decode('utf-8') 9 | string = re.sub("[\s+\.\!\/_,$%^*(+\"\']+|[+——!:,。?、~@#¥%……&*()]+".decode("utf-8"), "".decode("utf-8"),string) 10 | string = string.encode('utf-8') 11 | string = string.translate(None,punctuation+digits+letters+whitespace) 12 | seg = jieba.cut(string) 13 | return ' '.join(seg) 14 | print('ddddddddddddd') 15 | 16 | -------------------------------------------------------------------------------- /myuntils.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/myuntils.pyc -------------------------------------------------------------------------------- /networkPublicOpinionAnalysisSystem/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/networkPublicOpinionAnalysisSystem/__init__.py -------------------------------------------------------------------------------- /networkPublicOpinionAnalysisSystem/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/networkPublicOpinionAnalysisSystem/__init__.pyc -------------------------------------------------------------------------------- /networkPublicOpinionAnalysisSystem/settings.py: -------------------------------------------------------------------------------- 1 | """ 2 | Django settings for networkPublicOpinionAnalysisSystem project. 3 | 4 | Generated by 'django-admin startproject' using Django 1.9.1. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/1.9/topics/settings/ 8 | 9 | For the full list of settings and their values, see 10 | https://docs.djangoproject.com/en/1.9/ref/settings/ 11 | """ 12 | 13 | import os 14 | 15 | # Build paths inside the project like this: os.path.join(BASE_DIR, ...) 16 | BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) 17 | 18 | 19 | # Quick-start development settings - unsuitable for production 20 | # See https://docs.djangoproject.com/en/1.9/howto/deployment/checklist/ 21 | 22 | # SECURITY WARNING: keep the secret key used in production secret! 23 | SECRET_KEY = 'ekciy9oo!qu^yrfn^=!6o%6jdw2@5@m*tpp)35vx6l#hp1z(0y' 24 | 25 | # SECURITY WARNING: don't run with debug turned on in production! 26 | DEBUG = True 27 | 28 | ALLOWED_HOSTS = [] 29 | 30 | 31 | # Application definition 32 | 33 | INSTALLED_APPS = [ 34 | 'django.contrib.admin', 35 | 'django.contrib.auth', 36 | 'django.contrib.contenttypes', 37 | 'django.contrib.sessions', 38 | 'django.contrib.messages', 39 | 'django.contrib.staticfiles', 40 | 'learn', 41 | 'system' 42 | ] 43 | 44 | MIDDLEWARE_CLASSES = [ 45 | 'django.middleware.security.SecurityMiddleware', 46 | 'django.contrib.sessions.middleware.SessionMiddleware', 47 | 'django.middleware.common.CommonMiddleware', 48 | 'django.middleware.csrf.CsrfViewMiddleware', 49 | 'django.contrib.auth.middleware.AuthenticationMiddleware', 50 | 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 51 | 'django.contrib.messages.middleware.MessageMiddleware', 52 | 'django.middleware.clickjacking.XFrameOptionsMiddleware', 53 | ] 54 | 55 | ROOT_URLCONF = 'networkPublicOpinionAnalysisSystem.urls' 56 | 57 | TEMPLATES = [ 58 | { 59 | 'BACKEND': 'django.template.backends.django.DjangoTemplates', 60 | 'DIRS': [], 61 | 'APP_DIRS': True, 62 | 'OPTIONS': { 63 | 'context_processors': [ 64 | 'django.template.context_processors.debug', 65 | 'django.template.context_processors.request', 66 | 'django.contrib.auth.context_processors.auth', 67 | 'django.contrib.messages.context_processors.messages', 68 | ], 69 | }, 70 | }, 71 | ] 72 | 73 | WSGI_APPLICATION = 'networkPublicOpinionAnalysisSystem.wsgi.application' 74 | 75 | 76 | # Database 77 | # https://docs.djangoproject.com/en/1.9/ref/settings/#databases 78 | 79 | DATABASES = { 80 | 'default': { 81 | 'ENGINE': 'django.db.backends.sqlite3', 82 | 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), 83 | } 84 | } 85 | 86 | 87 | # Password validation 88 | # https://docs.djangoproject.com/en/1.9/ref/settings/#auth-password-validators 89 | 90 | AUTH_PASSWORD_VALIDATORS = [ 91 | { 92 | 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', 93 | }, 94 | { 95 | 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', 96 | }, 97 | { 98 | 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', 99 | }, 100 | { 101 | 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', 102 | }, 103 | ] 104 | 105 | 106 | # Internationalization 107 | # https://docs.djangoproject.com/en/1.9/topics/i18n/ 108 | 109 | LANGUAGE_CODE = 'en-us' 110 | 111 | TIME_ZONE = 'UTC' 112 | 113 | USE_I18N = True 114 | 115 | USE_L10N = True 116 | 117 | USE_TZ = True 118 | 119 | 120 | # Static files (CSS, JavaScript, Images) 121 | # https://docs.djangoproject.com/en/1.9/howto/static-files/ 122 | 123 | STATIC_URL = '/static/' 124 | -------------------------------------------------------------------------------- /networkPublicOpinionAnalysisSystem/settings.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/networkPublicOpinionAnalysisSystem/settings.pyc -------------------------------------------------------------------------------- /networkPublicOpinionAnalysisSystem/urls.py: -------------------------------------------------------------------------------- 1 | """networkPublicOpinionAnalysisSystem URL Configuration 2 | 3 | The `urlpatterns` list routes URLs to views. For more information please see: 4 | https://docs.djangoproject.com/en/1.9/topics/http/urls/ 5 | Examples: 6 | Function views 7 | 1. Add an import: from my_app import views 8 | 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') 9 | Class-based views 10 | 1. Add an import: from other_app.views import Home 11 | 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') 12 | Including another URLconf 13 | 1. Import the include() function: from django.conf.urls import url, include 14 | 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) 15 | """ 16 | from django.conf.urls import url 17 | from django.contrib import admin 18 | from learn import views as learn_views 19 | from system import views as system_views 20 | 21 | urlpatterns = [ 22 | url(r'^admin/', admin.site.urls), 23 | url(r'^index',system_views.index), 24 | url(r'^search',system_views.search), 25 | url(r'^showAsLabel',system_views.showAsLabel), 26 | ] 27 | -------------------------------------------------------------------------------- /networkPublicOpinionAnalysisSystem/urls.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/networkPublicOpinionAnalysisSystem/urls.pyc -------------------------------------------------------------------------------- /networkPublicOpinionAnalysisSystem/wsgi.py: -------------------------------------------------------------------------------- 1 | """ 2 | WSGI config for networkPublicOpinionAnalysisSystem project. 3 | 4 | It exposes the WSGI callable as a module-level variable named ``application``. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/1.9/howto/deployment/wsgi/ 8 | """ 9 | 10 | import os 11 | 12 | from django.core.wsgi import get_wsgi_application 13 | 14 | os.environ.setdefault("DJANGO_SETTINGS_MODULE", "networkPublicOpinionAnalysisSystem.settings") 15 | 16 | application = get_wsgi_application() 17 | -------------------------------------------------------------------------------- /networkPublicOpinionAnalysisSystem/wsgi.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/networkPublicOpinionAnalysisSystem/wsgi.pyc -------------------------------------------------------------------------------- /sample_libsvm_data.txt: -------------------------------------------------------------------------------- 1 | 0 128:51 129:159 130:253 131:159 132:50 155:48 156:238 157:252 158:252 159:252 160:237 182:54 183:227 184:253 185:252 186:239 187:233 188:252 189:57 190:6 208:10 209:60 210:224 211:252 212:253 213:252 214:202 215:84 216:252 217:253 218:122 236:163 237:252 238:252 239:252 240:253 241:252 242:252 243:96 244:189 245:253 246:167 263:51 264:238 265:253 266:253 267:190 268:114 269:253 270:228 271:47 272:79 273:255 274:168 290:48 291:238 292:252 293:252 294:179 295:12 296:75 297:121 298:21 301:253 302:243 303:50 317:38 318:165 319:253 320:233 321:208 322:84 329:253 330:252 331:165 344:7 345:178 346:252 347:240 348:71 349:19 350:28 357:253 358:252 359:195 372:57 373:252 374:252 375:63 385:253 386:252 387:195 400:198 401:253 402:190 413:255 414:253 415:196 427:76 428:246 429:252 430:112 441:253 442:252 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375:127 383:99 384:253 385:253 386:253 387:116 399:176 400:253 401:253 402:131 403:9 411:99 412:253 413:253 414:253 415:116 426:119 427:254 428:254 429:232 430:75 440:158 441:254 442:254 443:117 454:118 455:253 456:253 457:154 468:156 469:253 470:253 471:116 482:118 483:253 484:253 485:154 496:156 497:253 498:253 499:116 509:46 510:222 511:253 512:253 513:154 522:7 523:116 524:246 525:253 526:180 527:9 538:118 539:253 540:253 541:154 550:116 551:253 552:253 553:253 554:174 566:118 567:253 568:253 569:154 577:110 578:246 579:253 580:253 581:240 582:67 594:118 595:253 596:253 597:238 598:215 599:49 600:20 601:20 602:20 603:66 604:215 605:241 606:253 607:245 608:233 609:64 622:82 623:229 624:253 625:253 626:253 627:253 628:253 629:253 630:253 631:254 632:253 633:253 634:240 635:107 651:176 652:253 653:253 654:253 655:253 656:253 657:253 658:253 659:254 660:253 661:253 662:108 679:40 680:239 681:253 682:253 683:253 684:253 685:253 686:253 687:254 688:161 689:57 690:4 13 | 0 152:56 153:105 154:220 155:254 156:63 178:18 179:166 180:233 181:253 182:253 183:253 184:236 185:209 186:209 187:209 188:77 189:18 206:84 207:253 208:253 209:253 210:253 211:253 212:254 213:253 214:253 215:253 216:253 217:172 218:8 233:57 234:238 235:253 236:253 237:253 238:253 239:253 240:254 241:253 242:253 243:253 244:253 245:253 246:119 260:14 261:238 262:253 263:253 264:253 265:253 266:253 267:253 268:179 269:196 270:253 271:253 272:253 273:253 274:238 275:12 288:33 289:253 290:253 291:253 292:253 293:253 294:248 295:134 297:18 298:83 299:237 300:253 301:253 302:253 303:14 316:164 317:253 318:253 319:253 320:253 321:253 322:128 327:57 328:119 329:214 330:253 331:94 343:57 344:248 345:253 346:253 347:253 348:126 349:14 350:4 357:179 358:253 359:248 360:56 371:175 372:253 373:253 374:240 375:190 376:28 385:179 386:253 387:253 388:173 399:209 400:253 401:253 402:178 413:92 414:253 415:253 416:208 427:211 428:254 429:254 430:179 442:135 443:255 444:209 455:209 456:253 457:253 458:90 470:134 471:253 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354:73 377:54 378:229 379:253 380:254 381:105 405:152 406:254 407:254 408:213 409:26 432:32 433:243 434:253 435:253 436:115 459:2 460:142 461:253 462:253 463:155 487:30 488:253 489:253 490:232 491:55 515:75 516:253 517:253 518:164 542:72 543:232 544:253 545:189 546:17 570:224 571:253 572:253 573:163 597:43 598:235 599:253 600:253 601:195 602:21 625:28 626:231 627:253 628:253 629:184 630:14 654:225 655:253 656:253 657:75 15 | 0 155:21 156:176 157:253 158:253 159:124 182:105 183:176 184:251 185:251 186:251 187:251 188:105 208:58 209:217 210:241 211:253 212:251 213:251 214:251 215:251 216:243 217:113 218:5 235:63 236:231 237:251 238:251 239:253 240:251 241:251 242:251 243:251 244:253 245:251 246:113 263:144 264:251 265:251 266:251 267:253 268:251 269:251 270:251 271:251 272:253 273:251 274:215 290:125 291:253 292:253 293:253 294:253 295:255 296:253 297:253 298:253 299:253 300:255 301:253 302:227 303:42 318:253 319:251 320:251 321:251 322:251 323:253 324:251 325:251 326:251 327:251 328:253 329:251 330:251 331:142 345:27 346:253 347:251 348:251 349:235 350:241 351:253 352:251 353:246 354:137 355:35 356:98 357:251 358:251 359:236 360:61 372:47 373:211 374:253 375:251 376:235 377:82 378:103 379:253 380:251 381:137 384:73 385:251 386:251 387:251 388:71 399:27 400:211 401:251 402:253 403:251 404:86 407:72 408:71 409:10 412:73 413:251 414:251 415:173 416:20 427:89 428:253 429:253 430:255 431:253 432:35 440:73 441:253 442:253 443:253 444:72 454:84 455:236 456:251 457:251 458:253 459:251 460:138 468:73 469:251 470:251 471:251 472:71 481:63 482:236 483:251 484:251 485:251 486:227 487:251 488:246 489:138 490:11 494:16 495:37 496:228 497:251 498:246 499:137 500:10 509:73 510:251 511:251 512:251 513:173 514:42 515:142 516:142 517:142 518:41 522:109 523:251 524:253 525:251 526:137 537:73 538:251 539:251 540:173 541:20 549:27 550:211 551:251 552:253 553:147 554:10 565:73 566:253 567:253 568:143 575:21 576:176 577:253 578:253 579:253 593:73 594:251 595:251 596:205 597:144 603:176 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627:166 628:254 629:210 630:23 655:223 656:252 657:104 683:223 684:169 17 | 0 125:29 126:170 127:255 128:255 129:141 151:29 152:198 153:255 154:255 155:255 156:226 157:255 158:86 178:141 179:255 180:255 181:170 182:29 184:86 185:255 186:255 187:141 204:29 205:226 206:255 207:198 208:57 213:226 214:255 215:255 216:226 217:114 231:29 232:255 233:255 234:114 241:141 242:170 243:114 244:255 245:255 246:141 259:226 260:255 261:170 269:29 270:57 273:141 274:255 275:226 286:57 287:255 288:170 302:114 303:255 304:198 314:226 315:255 331:170 332:255 333:57 342:255 343:226 360:255 361:170 370:255 371:170 388:114 389:198 398:255 399:226 416:86 417:255 426:198 427:255 444:86 445:255 454:114 455:255 456:57 472:86 473:255 482:29 483:255 484:226 500:141 501:255 511:170 512:255 513:170 528:226 529:198 539:29 540:226 541:255 542:170 555:29 556:255 557:114 568:29 569:226 570:255 571:141 582:57 583:226 584:226 598:141 599:255 600:255 601:170 602:86 607:29 608:86 609:226 610:255 611:226 612:29 627:86 628:198 629:255 630:255 631:255 632:255 633:255 634:255 635:255 636:255 637:255 638:141 639:29 657:29 658:114 659:170 660:170 661:170 662:170 663:170 664:86 18 | 0 153:203 154:254 155:252 156:252 157:252 158:214 159:51 160:20 180:62 181:221 182:252 183:250 184:250 185:250 186:252 187:250 188:160 189:20 207:62 208:211 209:250 210:252 211:250 212:250 213:250 214:252 215:250 216:250 217:49 234:41 235:221 236:250 237:250 238:252 239:250 240:250 241:250 242:252 243:250 244:128 245:10 262:254 263:252 264:252 265:252 266:254 267:252 268:252 269:252 270:254 271:252 272:252 273:90 290:150 291:190 292:250 293:250 294:252 295:250 296:250 297:169 298:171 299:250 300:250 301:250 302:82 318:31 319:191 320:250 321:250 322:252 323:189 324:100 325:20 326:172 327:250 328:250 329:250 330:80 346:213 347:250 348:250 349:250 350:212 351:29 354:252 355:250 356:250 357:250 374:92 375:252 376:252 377:252 382:51 383:252 384:252 385:252 386:203 401:82 402:252 403:250 404:250 405:169 410:132 411:250 412:250 413:250 414:121 428:92 429:231 430:252 431:250 432:159 433:20 438:252 439:250 440:250 441:250 456:30 457:211 458:252 459:250 460:221 461:40 466:90 467:250 468:250 469:250 470:163 484:31 485:213 486:254 487:232 488:80 494:92 495:252 496:252 497:212 498:163 512:151 513:250 514:252 515:149 522:252 523:250 524:250 525:49 540:60 541:221 542:252 543:210 544:60 550:252 551:250 552:250 553:49 569:202 570:252 571:250 572:221 573:40 576:123 577:202 578:252 579:250 580:250 581:49 596:123 597:243 598:255 599:252 600:252 601:252 602:254 603:252 604:252 605:252 606:254 607:252 608:100 625:121 626:171 627:250 628:250 629:250 630:252 631:250 632:250 633:250 634:252 635:250 636:100 654:20 655:160 656:250 657:250 658:252 659:250 660:250 661:250 662:252 663:189 664:40 683:20 684:170 685:250 686:252 687:250 688:128 689:49 690:49 691:29 19 | 1 98:64 99:191 100:70 125:68 126:243 127:253 128:249 129:63 152:30 153:223 154:253 155:253 156:247 157:41 179:73 180:238 181:253 182:253 183:253 184:242 206:73 207:236 208:253 209:253 210:253 211:253 212:242 234:182 235:253 236:253 237:191 238:247 239:253 240:149 262:141 263:253 264:143 265:86 266:249 267:253 268:122 290:9 291:36 292:7 293:14 294:233 295:253 296:122 322:230 323:253 324:122 350:230 351:253 352:122 378:231 379:255 380:123 406:230 407:253 408:52 433:61 434:245 435:253 461:98 462:253 463:253 468:35 469:12 489:98 490:253 491:253 494:9 495:142 496:233 497:146 517:190 518:253 519:253 520:128 521:7 522:99 523:253 524:253 525:180 544:29 545:230 546:253 547:253 548:252 549:210 550:253 551:253 552:253 553:140 571:28 572:207 573:253 574:253 575:253 576:254 577:253 578:253 579:235 580:70 581:9 599:126 600:253 601:253 602:253 603:253 604:254 605:253 606:168 607:19 627:79 628:253 629:253 630:201 631:190 632:132 633:63 634:5 20 | 1 125:26 126:240 127:72 153:25 154:238 155:208 182:209 183:226 184:14 210:209 211:254 212:43 238:175 239:254 240:128 266:63 267:254 268:204 294:107 295:254 296:204 322:88 323:254 324:204 350:55 351:254 352:204 378:126 379:254 380:204 406:126 407:254 408:189 434:169 435:254 436:121 462:209 463:254 464:193 490:209 491:254 492:111 517:22 518:235 519:254 520:37 545:137 546:254 547:227 548:16 573:205 574:255 575:185 601:205 602:254 603:125 629:205 630:254 631:125 657:111 658:212 659:43 21 | 0 155:62 156:91 157:213 158:255 159:228 160:91 161:12 182:70 183:230 184:253 185:253 186:253 187:253 188:253 189:152 190:7 210:246 211:253 212:253 213:253 214:253 215:253 216:253 217:253 218:106 237:21 238:247 239:253 240:253 241:253 242:253 243:253 244:253 245:208 246:24 265:156 266:253 267:253 268:253 269:253 270:253 271:253 272:253 273:195 292:88 293:238 294:253 295:253 296:253 297:221 298:253 299:253 300:253 301:195 320:230 321:253 322:253 323:253 324:198 325:40 326:177 327:253 328:253 329:195 346:56 347:156 348:251 349:253 350:189 351:182 352:15 354:86 355:240 356:253 357:210 358:28 374:213 375:253 376:253 377:156 378:3 383:205 384:253 385:253 386:106 401:121 402:252 403:253 404:135 405:3 411:46 412:253 413:253 414:106 428:28 429:212 430:253 431:248 432:23 439:42 440:253 441:253 442:106 456:197 457:253 458:234 459:70 467:42 468:253 469:253 470:106 483:11 484:202 485:253 486:187 495:58 496:253 497:210 498:27 511:107 512:253 513:253 514:40 522:53 523:227 524:253 525:195 539:107 540:253 541:253 542:40 549:47 550:227 551:253 552:231 553:58 567:107 568:253 569:253 570:40 575:5 576:131 577:222 578:253 579:231 580:59 595:14 596:204 597:253 598:226 599:222 600:73 601:58 602:58 603:170 604:253 605:253 606:227 607:58 624:197 625:253 626:253 627:253 628:253 629:253 630:253 631:253 632:253 633:238 634:58 652:33 653:179 654:241 655:253 656:253 657:253 658:253 659:250 660:116 661:14 682:75 683:179 684:253 685:151 686:89 687:86 22 | 1 157:42 158:228 159:253 160:253 185:144 186:251 187:251 188:251 212:89 213:236 214:251 215:235 216:215 239:79 240:253 241:251 242:251 243:142 267:180 268:253 269:251 270:251 271:142 294:32 295:202 296:255 297:253 298:216 322:109 323:251 324:253 325:251 326:112 349:6 350:129 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385:249 386:58 401:16 402:168 403:253 404:216 405:45 410:53 411:253 412:253 413:138 429:159 430:253 431:253 432:147 438:53 439:253 440:253 441:138 456:136 457:252 458:253 459:227 460:5 466:53 467:253 468:243 469:101 484:140 485:253 486:253 487:124 494:156 495:253 496:218 511:13 512:164 513:253 514:142 515:5 521:32 522:233 523:253 524:218 539:62 540:253 541:253 542:130 548:37 549:203 550:253 551:253 552:127 567:62 568:253 569:253 570:147 571:36 572:36 573:36 574:36 575:151 576:222 577:253 578:245 579:127 580:8 595:34 596:202 597:253 598:253 599:253 600:253 601:253 602:253 603:253 604:253 605:253 606:200 624:140 625:253 626:253 627:253 628:253 629:253 630:253 631:253 632:248 633:235 634:65 652:87 653:173 654:253 655:253 656:253 657:253 658:253 659:253 660:182 681:14 682:78 683:96 684:253 685:253 686:253 687:137 688:56 25 | 0 123:8 124:76 125:202 126:254 127:255 128:163 129:37 130:2 150:13 151:182 152:253 153:253 154:253 155:253 156:253 157:253 158:23 177:15 178:179 179:253 180:253 181:212 182:91 183:218 184:253 185:253 186:179 187:109 205:105 206:253 207:253 208:160 209:35 210:156 211:253 212:253 213:253 214:253 215:250 216:113 232:19 233:212 234:253 235:253 236:88 237:121 238:253 239:233 240:128 241:91 242:245 243:253 244:248 245:114 260:104 261:253 262:253 263:110 264:2 265:142 266:253 267:90 270:26 271:199 272:253 273:248 274:63 287:1 288:173 289:253 290:253 291:29 293:84 294:228 295:39 299:72 300:251 301:253 302:215 303:29 315:36 316:253 317:253 318:203 319:13 328:82 329:253 330:253 331:170 343:36 344:253 345:253 346:164 356:11 357:198 358:253 359:184 360:6 371:36 372:253 373:253 374:82 385:138 386:253 387:253 388:35 399:128 400:253 401:253 402:47 413:48 414:253 415:253 416:35 427:154 428:253 429:253 430:47 441:48 442:253 443:253 444:35 455:102 456:253 457:253 458:99 469:48 470:253 471:253 472:35 483:36 484:253 485:253 486:164 496:16 497:208 498:253 499:211 500:17 511:32 512:244 513:253 514:175 515:4 524:44 525:253 526:253 527:156 540:171 541:253 542:253 543:29 551:30 552:217 553:253 554:188 555:19 568:171 569:253 570:253 571:59 578:60 579:217 580:253 581:253 582:70 596:78 597:253 598:253 599:231 600:48 604:26 605:128 606:249 607:253 608:244 609:94 610:15 624:8 625:151 626:253 627:253 628:234 629:101 630:121 631:219 632:229 633:253 634:253 635:201 636:80 653:38 654:232 655:253 656:253 657:253 658:253 659:253 660:253 661:253 662:201 663:66 26 | 0 127:68 128:254 129:255 130:254 131:107 153:11 154:176 155:230 156:253 157:253 158:253 159:212 180:28 181:197 182:253 183:253 184:253 185:253 186:253 187:229 188:107 189:14 208:194 209:253 210:253 211:253 212:253 213:253 214:253 215:253 216:253 217:53 235:69 236:241 237:253 238:253 239:253 240:253 241:241 242:186 243:253 244:253 245:195 262:10 263:161 264:253 265:253 266:253 267:246 268:40 269:57 270:231 271:253 272:253 273:195 290:140 291:253 292:253 293:253 294:253 295:154 297:25 298:253 299:253 300:253 301:195 318:213 319:253 320:253 321:253 322:135 323:8 325:3 326:128 327:253 328:253 329:195 345:77 346:238 347:253 348:253 349:253 350:7 354:116 355:253 356:253 357:195 372:11 373:165 374:253 375:253 376:231 377:70 378:1 382:78 383:237 384:253 385:195 400:33 401:253 402:253 403:253 404:182 411:200 412:253 413:195 428:98 429:253 430:253 431:253 432:24 439:42 440:253 441:195 456:197 457:253 458:253 459:253 460:24 467:163 468:253 469:195 484:197 485:253 486:253 487:189 488:13 494:53 495:227 496:253 497:121 512:197 513:253 514:253 515:114 521:21 522:227 523:253 524:231 525:27 540:197 541:253 542:253 543:114 547:5 548:131 549:143 550:253 551:231 552:59 568:197 569:253 570:253 571:236 572:73 573:58 574:217 575:223 576:253 577:253 578:253 579:174 596:197 597:253 598:253 599:253 600:253 601:253 602:253 603:253 604:253 605:253 606:253 607:48 624:149 625:253 626:253 627:253 628:253 629:253 630:253 631:253 632:253 633:182 634:15 635:3 652:12 653:168 654:253 655:253 656:253 657:253 658:253 659:248 660:89 661:23 27 | 1 157:85 158:255 159:103 160:1 185:205 186:253 187:253 188:30 213:205 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324:176 325:6 350:32 351:253 352:233 353:43 378:140 379:253 380:195 381:19 406:140 407:253 408:167 433:29 434:253 435:141 461:29 462:252 463:140 489:29 490:252 491:140 517:29 518:252 519:140 545:29 546:252 547:140 573:169 574:253 575:79 601:169 602:252 628:76 629:234 630:141 656:197 657:233 658:37 684:197 685:223 39 | 1 127:73 128:253 129:253 130:63 155:115 156:252 157:252 158:144 183:217 184:252 185:252 186:144 210:63 211:237 212:252 213:252 214:144 238:109 239:252 240:252 241:252 266:109 267:252 268:252 269:252 294:109 295:252 296:252 297:252 322:191 323:252 324:252 325:252 349:145 350:255 351:253 352:253 353:253 376:32 377:237 378:253 379:252 380:252 381:210 404:37 405:252 406:253 407:252 408:252 409:108 432:37 433:252 434:253 435:252 436:252 437:108 460:21 461:207 462:255 463:253 464:253 465:108 489:144 490:253 491:252 492:252 493:108 516:27 517:221 518:253 519:252 520:252 521:108 544:16 545:190 546:253 547:252 548:252 549:108 573:145 574:255 575:253 576:253 577:253 601:144 602:253 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246:253 247:246 248:187 249:13 261:26 262:255 263:253 264:244 265:175 266:101 274:126 275:244 276:253 277:153 288:82 289:243 290:253 291:214 292:81 303:169 304:252 305:252 315:19 316:215 317:252 318:206 319:56 331:169 332:252 333:252 343:157 344:252 345:252 346:13 359:169 360:252 361:151 370:41 371:253 372:253 373:128 386:92 387:253 388:206 389:13 398:166 399:252 400:196 401:9 414:216 415:252 416:142 426:253 427:252 428:168 441:89 442:253 443:208 444:13 454:253 455:252 456:68 468:38 469:225 470:253 471:96 482:254 483:253 484:56 495:45 496:229 497:253 498:151 510:253 511:252 512:81 522:70 523:225 524:252 525:227 538:216 539:252 540:168 548:29 549:134 550:253 551:252 552:186 553:31 566:91 567:252 568:243 569:125 573:51 574:114 575:113 576:210 577:252 578:253 579:151 580:19 595:157 596:253 597:253 598:254 599:253 600:253 601:253 602:254 603:253 604:244 605:175 606:51 623:19 624:122 625:196 626:197 627:221 628:196 629:196 630:197 631:121 632:56 655:25 43 | 0 127:42 128:235 129:255 130:84 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235:227 236:253 237:253 238:253 239:92 240:38 241:226 242:253 243:129 244:2 263:193 264:253 265:253 266:248 267:62 269:50 270:253 271:253 272:45 291:170 292:253 293:253 294:135 297:12 298:208 299:253 300:119 318:16 319:232 320:253 321:253 322:21 326:60 327:253 328:185 346:164 347:253 348:253 349:224 350:14 354:14 355:217 356:247 357:62 373:3 374:193 375:253 376:250 377:64 383:199 384:253 385:179 401:67 402:253 403:253 404:205 411:98 412:253 413:188 429:151 430:253 431:245 432:43 439:63 440:250 441:188 457:151 458:253 459:243 468:244 469:222 470:22 485:151 486:253 487:217 496:244 497:253 498:115 512:3 513:195 514:253 515:134 524:156 525:253 526:150 541:140 542:253 543:134 552:239 553:253 554:139 569:44 570:253 571:134 579:53 580:246 581:237 582:32 597:8 598:200 599:229 600:40 606:25 607:225 608:253 609:188 626:120 627:250 628:230 629:58 630:17 632:12 633:42 634:213 635:253 636:238 637:84 655:151 656:253 657:253 658:217 659:179 660:206 661:253 662:253 663:196 664:118 683:18 684:58 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576:246 577:101 602:145 603:253 604:255 605:92 630:93 631:253 632:246 633:59 658:93 659:253 660:74 91 | 0 127:46 128:105 129:254 130:254 131:224 132:59 133:59 134:9 155:196 156:254 157:253 158:253 159:253 160:253 161:253 162:128 182:96 183:235 184:254 185:253 186:253 187:253 188:253 189:253 190:247 191:122 208:4 209:101 210:244 211:253 212:254 213:234 214:241 215:253 216:253 217:253 218:253 219:186 220:18 236:96 237:253 238:253 239:253 240:232 241:83 242:109 243:170 244:253 245:253 246:253 247:253 248:116 264:215 265:253 266:253 267:253 268:196 271:40 272:253 273:253 274:253 275:253 276:116 290:8 291:141 292:247 293:253 294:253 295:237 296:29 299:6 300:38 301:171 302:253 303:253 304:116 317:13 318:146 319:253 320:253 321:253 322:253 323:57 329:156 330:253 331:253 332:116 345:40 346:253 347:253 348:253 349:253 350:178 351:27 357:156 358:253 359:253 360:116 372:136 373:204 374:253 375:253 376:253 377:192 378:27 385:156 386:253 387:253 388:116 399:28 400:195 401:254 402:254 403:254 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430:71 441:73 442:251 443:251 444:251 445:71 454:253 455:251 456:251 457:157 458:10 469:73 470:251 471:251 472:251 473:71 482:253 483:251 484:251 485:142 497:150 498:251 499:251 500:204 501:41 510:124 511:251 512:251 513:220 514:180 524:130 525:253 526:251 527:225 528:41 538:73 539:253 540:253 541:253 542:253 543:73 544:73 545:10 549:42 550:73 551:150 552:253 553:255 554:253 555:216 566:31 567:189 568:251 569:251 570:251 571:253 572:251 573:159 574:144 575:144 576:145 577:206 578:251 579:251 580:251 581:253 582:168 583:92 595:20 596:195 597:251 598:251 599:253 600:251 601:251 602:251 603:251 604:253 605:251 606:251 607:251 608:225 609:164 610:15 624:21 625:142 626:220 627:253 628:251 629:251 630:251 631:251 632:253 633:251 634:251 635:204 636:41 654:51 655:72 656:174 657:251 658:251 659:251 660:253 661:147 662:71 663:41 97 | 0 127:60 128:96 129:96 130:48 153:16 154:171 155:228 156:253 157:251 158:220 159:51 160:32 181:127 182:251 183:251 184:253 185:251 186:251 187:251 188:251 189:80 207:24 208:182 209:236 210:251 211:211 212:189 213:236 214:251 215:251 216:251 217:242 218:193 234:100 235:194 236:251 237:251 238:211 239:35 241:71 242:173 243:251 244:251 245:253 246:240 247:158 248:19 261:64 262:253 263:255 264:253 265:205 266:19 271:40 272:218 273:255 274:253 275:253 276:91 288:16 289:186 290:251 291:253 292:247 293:110 300:39 301:233 302:251 303:251 304:188 315:16 316:189 317:251 318:251 319:205 320:110 329:48 330:220 331:251 332:220 333:48 343:72 344:251 345:251 346:251 347:158 358:51 359:251 360:251 361:232 371:190 372:251 373:251 374:251 375:59 386:32 387:251 388:251 389:251 398:96 399:253 400:253 401:253 402:95 414:32 415:253 416:253 417:193 426:214 427:251 428:251 429:204 430:23 442:52 443:251 444:251 445:94 454:253 455:251 456:251 457:109 469:48 470:221 471:251 472:219 473:47 482:253 483:251 484:251 485:70 497:234 498:251 499:251 500:188 510:253 511:251 512:251 513:188 523:40 524:158 525:253 526:251 527:172 528:70 539:191 540:253 541:253 542:253 543:96 544:24 549:12 550:174 551:253 552:253 553:255 554:221 567:71 568:251 569:251 570:251 571:253 572:205 573:190 574:190 575:190 576:191 577:197 578:251 579:251 580:231 581:221 582:93 595:16 596:126 597:236 598:251 599:253 600:251 601:251 602:251 603:251 604:253 605:251 606:251 607:140 608:47 625:67 626:188 627:189 628:188 629:188 630:188 631:188 632:189 633:188 634:109 635:4 98 | 0 126:32 127:202 128:255 129:253 130:253 131:175 132:21 152:84 153:144 154:190 155:251 156:253 157:251 158:251 159:251 160:174 176:6 177:37 178:166 179:218 180:236 181:251 182:251 183:251 184:253 185:251 186:251 187:251 188:251 189:156 204:115 205:251 206:251 207:253 208:251 209:251 210:251 211:251 212:253 213:251 214:251 215:251 216:251 217:180 231:105 232:241 233:251 234:251 235:253 236:251 237:251 238:251 239:122 240:72 241:71 242:71 243:148 244:251 245:180 258:73 259:253 260:253 261:253 262:253 263:202 264:253 265:253 266:143 286:31 287:189 288:251 289:251 290:251 291:31 292:189 293:251 294:142 314:63 315:236 316:251 317:251 318:96 320:124 321:246 322:142 330:21 331:166 332:21 342:73 343:251 344:251 345:251 346:71 349:217 350:142 357:32 358:190 359:251 360:142 370:73 371:251 372:251 373:251 374:71 377:217 378:142 385:73 386:251 387:251 388:142 398:73 399:253 400:253 401:253 402:72 405:156 406:103 413:73 414:253 415:253 416:253 417:72 426:73 427:251 428:251 429:251 430:174 441:73 442:251 443:251 444:251 445:71 454:73 455:251 456:251 457:251 458:251 469:73 470:251 471:251 472:251 473:71 482:42 483:205 484:251 485:251 486:251 487:79 497:73 498:251 499:251 500:251 501:71 511:41 512:226 513:251 514:251 515:232 516:77 525:73 526:251 527:251 528:251 529:71 540:166 541:253 542:253 543:255 544:253 545:227 546:73 547:21 553:125 554:253 555:253 556:143 568:16 569:169 570:251 571:253 572:251 573:251 574:251 575:174 576:105 579:63 580:144 581:253 582:251 583:251 584:142 597:15 598:35 599:253 600:251 601:251 602:251 603:251 604:243 605:217 606:217 607:231 608:251 609:253 610:251 611:220 612:20 627:143 628:142 629:236 630:251 631:251 632:253 633:251 634:251 635:251 636:251 637:253 638:251 639:137 657:61 658:71 659:200 660:253 661:251 662:251 663:251 664:251 665:201 666:71 667:10 99 | 1 130:218 131:170 132:108 157:32 158:227 159:252 160:232 185:129 186:252 187:252 188:252 212:1 213:253 214:252 215:252 216:168 240:144 241:253 242:252 243:236 244:62 268:144 269:253 270:252 271:215 296:144 297:253 298:252 299:112 323:21 324:206 325:253 326:252 327:71 351:99 352:253 353:255 354:119 378:63 379:242 380:252 381:253 382:35 406:94 407:252 408:252 409:154 410:10 433:145 434:237 435:252 436:252 461:255 462:253 463:253 464:108 487:11 488:155 489:253 490:252 491:179 492:15 514:11 515:150 516:252 517:253 518:200 519:20 542:73 543:252 544:252 545:253 546:97 569:47 570:233 571:253 572:253 596:1 597:149 598:252 599:252 600:252 624:1 625:252 626:252 627:246 628:132 652:1 653:169 654:252 655:132 100 | 1 130:116 131:255 132:123 157:29 158:213 159:253 160:122 185:189 186:253 187:253 188:122 213:189 214:253 215:253 216:122 241:189 242:253 243:253 244:122 267:2 268:114 269:243 270:253 271:186 272:19 295:100 296:253 297:253 298:253 299:48 323:172 324:253 325:253 326:253 327:48 351:172 352:253 353:253 354:182 355:19 378:133 379:251 380:253 381:175 382:4 405:107 406:251 407:253 408:253 409:65 432:26 433:194 434:253 435:253 436:214 437:40 459:105 460:205 461:253 462:253 463:125 464:40 487:139 488:253 489:253 490:253 491:81 514:41 515:231 516:253 517:253 518:159 519:16 541:65 542:155 543:253 544:253 545:172 546:4 569:124 570:253 571:253 572:253 573:98 597:124 598:253 599:253 600:214 601:41 624:22 625:207 626:253 627:253 628:139 653:124 654:253 655:162 656:9 101 | -------------------------------------------------------------------------------- /select.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | """SQLContext 数据查询""" 3 | import jieba 4 | import re 5 | from pyspark import SparkConf,SparkContext 6 | from pyspark.sql import SQLContext,Row,DataFrame,functions 7 | 8 | def showFirstNnews(DataFrame): 9 | """显示数据源下的前10条新闻,按时间排序""" 10 | temp = DataFrame.orderBy('time',ascending=1).limit(10).select("*") 11 | return convertDfToList(temp) 12 | 13 | def showNewsByCategory(DataFrame): 14 | """某一种类下的所有新闻,按时间排序""" 15 | temp = DataFrame.orderBy('time',ascending=1).where(DataFrame['label']==u'科技').select("*") 16 | return convertDfToList(temp) 17 | 18 | def convertDfToList(DataFrame): 19 | l = DataFrame.take(DataFrame.count()) 20 | myList = [] 21 | for row in l: 22 | dic = row.asDict() 23 | myList.append(dic) 24 | return myList 25 | 26 | conf = SparkConf().setAppName('tfidf').setMaster('spark://HP-Pavilion:7077') 27 | sc = SparkContext(conf=conf) 28 | sqlContext = SQLContext(sc) 29 | 30 | rawNews = sc.textFile(name='roll_news_sina_com_cn.csv') 31 | parts = rawNews.map(lambda line:line.split(',')) 32 | #titleNews 为 unicode 33 | titleNews = parts.map(lambda p:Row(label=p[0],title=p[1],url=p[2],time=p[3])) 34 | dfTitleNews = sqlContext.createDataFrame(titleNews) 35 | dfTitleNews.show 36 | dfTitleNews.printSchema() 37 | dfTitleNews.select(u'label').orderBy('time').show() 38 | dfTitleNews.orderBy('time').show() 39 | dfTitleNews.filter(dfTitleNews['label']==u'体育').show 40 | dfTitleNews.groupBy(u'label').count().show() 41 | dfTitleNews.drop('title').show() 42 | dfTitleNews.freqItems(('label','title')).show() 43 | dfTitleNews.filter(dfTitleNews['label']=='科技').limit(3).show() 44 | dfTitleNews.orderBy('time',ascending=0).show() 45 | dfTitleNews.orderBy('time',ascending=1).show() 46 | print(dfTitleNews.dtypes) 47 | print(showNewsByCategory(dfTitleNews)) 48 | 49 | sc.stop() -------------------------------------------------------------------------------- /selectTest.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/selectTest.py -------------------------------------------------------------------------------- /spider/roll_sina_spider.js: -------------------------------------------------------------------------------- 1 | //判断是否有下一页,有则进行渲染 2 | function hasNextPage(){ 3 | var a = document.querySelectorAll('#d_list .pagebox .pagebox_pre a')[1]; 4 | if(a!=null){ 5 | a.click(); 6 | return true; 7 | }else{ 8 | return false; 9 | } 10 | } 11 | 12 | //判断第一页是否有下一页,有则进行渲染 13 | function hasNextPageForFirstPage(){ 14 | var a = document.querySelector('#d_list .pagebox .pagebox_pre a'); 15 | if(a!=null){ 16 | a.click(); 17 | return true; 18 | }else{ 19 | return false; 20 | } 21 | } 22 | 23 | //获取单个页面的所有news 24 | function getAllNewsForSinglePage(){ 25 | 26 | var items = new Array(); 27 | //种类 28 | var categories = document.querySelectorAll('#d_list ul li .c_chl a'); 29 | //标题 30 | var titles = document.querySelectorAll('#d_list ul li .c_tit a'); 31 | //时间 32 | var times = document.querySelectorAll('#d_list ul li .c_time'); 33 | //url 34 | var urls = document.querySelectorAll('#d_list ul li .c_tit a'); 35 | var sum = categories.length; 36 | 37 | for(var i=0;i 2 | 3 | 4 | 系统首页 5 | 6 | 7 |

首页显示一定数目的新闻

8 | {% for item in sinaNewsList %} 9 | {{item.title}}
10 | {% endfor %} 11 |
12 | label:
13 | 14 |
15 | 16 | -------------------------------------------------------------------------------- /system/templates/system/search.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 查询功能 5 | 6 | 7 |

根据条件查询新闻

8 | {% for item in resultList %} 9 | {{item.title}}
10 | {% endfor %} 11 |
12 | label:
13 |
14 | 15 | -------------------------------------------------------------------------------- /system/templates/system/showAsLabel.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 查询功能 5 | 6 | 7 |

根据条件查询新闻

8 | {% for item in resultList %} 9 | {{item.title}}
10 | {% endfor %} 11 |
12 | label:
13 | 14 |
15 | 16 | -------------------------------------------------------------------------------- /system/tests.py: -------------------------------------------------------------------------------- 1 | from django.test import TestCase 2 | 3 | # Create your tests here. 4 | -------------------------------------------------------------------------------- /system/utils.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | """工具函数文件""" 3 | import jieba 4 | import re 5 | from pyspark import SparkConf,SparkContext 6 | from pyspark.sql import SQLContext,Row 7 | 8 | def createSinaNewsDF(): 9 | conf = SparkConf().setAppName('createSinaNewsDF').setMaster('spark://HP-Pavilion:7077') 10 | sc = SparkContext(conf=conf) 11 | sqlContext = SQLContext(sc) 12 | return sqlContext.read.parquet('roll_news_sina_com_cn.parquet') 13 | 14 | def showFirstNnews(DataFrame): 15 | """显示数据源下的前10条新闻,按时间排序""" 16 | temp = DataFrame.orderBy('time',ascending=1).limit(10).select("*") 17 | return convertDfToList(temp) 18 | 19 | def showNewsByCategory(DataFrame): 20 | """某一种类下的所有新闻,按时间排序""" 21 | temp = DataFrame.orderBy('time',ascending=1).where(DataFrame['label']==u'科技').select("*") 22 | return convertDfToList(temp) 23 | 24 | def convertDfToList(DataFrame): 25 | l = DataFrame.take(DataFrame.count()) 26 | myList = [] 27 | for row in l: 28 | dic = row.asDict() 29 | myList.append(dic) 30 | return myList 31 | 32 | conf = SparkConf().setAppName('tfidf').setMaster('spark://HP-Pavilion:7077') 33 | sc = SparkContext(conf=conf) 34 | sqlContext = SQLContext(sc) 35 | 36 | rawNews = sc.textFile(name='roll_news_sina_com_cn.csv') 37 | parts = rawNews.map(lambda line:line.split(',')) 38 | #titleNews 为 unicode 39 | titleNews = parts.map(lambda p:Row(label=p[0],title=p[1],url=p[2],time=p[3])) 40 | dfTitleNews = sqlContext.createDataFrame(titleNews) 41 | dfTitleNews.show 42 | dfTitleNews.printSchema() 43 | dfTitleNews.select(u'label').orderBy('time').show() 44 | dfTitleNews.orderBy('time').show() 45 | dfTitleNews.filter(dfTitleNews['label']==u'体育').show 46 | dfTitleNews.groupBy(u'label').count().show() 47 | dfTitleNews.drop('title').show() 48 | dfTitleNews.freqItems(('label','title')).show() 49 | dfTitleNews.filter(dfTitleNews['label']=='科技').limit(3).show() 50 | dfTitleNews.orderBy('time',ascending=0).show() 51 | dfTitleNews.orderBy('time',ascending=1).show() 52 | print(dfTitleNews.dtypes) 53 | print(showNewsByCategory(dfTitleNews)) 54 | 55 | sc.stop() -------------------------------------------------------------------------------- /system/views.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | from django.shortcuts import render 3 | from pyspark import SparkConf,SparkContext 4 | from pyspark.sql import SQLContext,Row 5 | from pyspark.ml.feature import HashingTF,IDF,Tokenizer 6 | from pyspark.ml import Pipeline 7 | from pyspark.ml.classification import DecisionTreeClassifier 8 | from pyspark.ml.feature import StringIndexer, VectorIndexer 9 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 10 | 11 | # Create your views here. 12 | 13 | def convertDfToList(DataFrame): 14 | """将 DataFrame 转换为 List""" 15 | l = DataFrame.take(DataFrame.count()) 16 | myList = [] 17 | for row in l: 18 | dic = row.asDict() 19 | myList.append(dic) 20 | return myList 21 | 22 | def index(request): 23 | """1.系统首页,显示各个数据源下前几条新闻""" 24 | conf = SparkConf().setAppName('index').setMaster('spark://HP-Pavilion:7077') 25 | sc = SparkContext(conf=conf) 26 | sqlContext = SQLContext(sc) 27 | rawNews = sc.textFile(name='data/roll_news_sina_com_cn.csv') 28 | parts = rawNews.map(lambda line:line.split(',')) 29 | titleNews = parts.map(lambda p:Row(label=p[0],title=p[1],url=p[2],time=p[3])) 30 | dfTitleNews = sqlContext.createDataFrame(titleNews) 31 | partions = dfTitleNews.orderBy('time',ascending=0).limit(10).select("*") 32 | sinaNewsList = convertDfToList(partions) 33 | print(sinaNewsList) 34 | sc.stop() 35 | return render(request,'system/index.html',{'sinaNewsList':sinaNewsList}) 36 | 37 | def search(request): 38 | """2.按查询条件返回结果""" 39 | label = request.POST['label'] 40 | kw = request.POST['kw'] 41 | time = request.POST['time'] 42 | 43 | conf = SparkConf().setAppName('search').setMaster('spark://HP-Pavilion:7077') 44 | sc = SparkContext(conf=conf) 45 | sqlContext = SQLContext(sc) 46 | 47 | rawNews = sc.textFile(name='data/roll_news_sina_com_cn.csv') 48 | parts = rawNews.map(lambda line:line.split(',')) 49 | titleNews = parts.map(lambda p:Row(label=p[0],title=p[1],url=p[2],time=p[3])) 50 | dfTitleNews = sqlContext.createDataFrame(titleNews) 51 | dfResult = dfTitleNews.where(dfTitleNews['label']==label).where(dfTitleNews['time']==time)\ 52 | .where(dfTitleNews['title']==kw).select("*") 53 | resultList = convertDfToList(dfResult) 54 | 55 | sc.stop() 56 | 57 | return render(request,'system/search.html',{'resultList':resultList}) 58 | 59 | def showAsLabel(request): 60 | """3.按标签显示""" 61 | label = request.GET['myLabel'] 62 | 63 | conf = SparkConf().setAppName('showAsLabel').setMaster('spark://HP-Pavilion:7077') 64 | sc = SparkContext(conf=conf) 65 | sqlContext = SQLContext(sc) 66 | 67 | rawNews = sc.textFile(name='data/roll_news_sina_com_cn.csv') 68 | parts = rawNews.map(lambda line:line.split(',')) 69 | titleNews = parts.map(lambda p:Row(label=p[0],title=p[1],url=p[2],time=p[3])) 70 | dfTitleNews = sqlContext.createDataFrame(titleNews) 71 | dfResult = dfTitleNews.where(dfTitleNews['label'] == label).select("*") 72 | resultList = convertDfToList(dfResult) 73 | print(resultList) 74 | sc.stop() 75 | 76 | return render(request,'system/showAsLabel.html',{'resultList':resultList}) 77 | 78 | def showWebsitesCrawled(request): 79 | """4.显示抓取的所有网站""" 80 | 81 | conf = SparkConf().setAppName('showWebsitesCrawled').setMaster('spark://HP-Pavilion:7077') 82 | sc = SparkContext(conf=conf) 83 | sqlContext = SQLContext(sc) 84 | 85 | rawNews = sc.textFile(name='data/websites_crawled.csv') 86 | parts = rawNews.map(lambda line:line.split(',')) 87 | temp = parts.map(lambda p:Row(title=p[0],url=p[1])) 88 | dfWebsites = sqlContext.createDataFrame(temp) 89 | resultList = convertDfToList(dfWebsites) 90 | 91 | sc.stop() 92 | 93 | return render(request,{'resultList':resultList}) 94 | 95 | def dataUpdate(request): 96 | """5.开启爬虫,实时抓取数据,更新数据源""" 97 | conf = SparkConf().setAppName('textPredict').setMaster('spark://HP-Pavilion:7077') 98 | sc = SparkContext(conf=conf) 99 | sqlContext = SQLContext(sc) 100 | 101 | sc.stop() 102 | 103 | 104 | def textPredict(request): 105 | """6.文本聚类,热度预测""" 106 | label = request.POST['label'] 107 | title = request.POST['title'] 108 | 109 | conf = SparkConf().setAppName('textPredict').setMaster('spark://HP-Pavilion:7077') 110 | sc = SparkContext(conf=conf) 111 | sqlContext = SQLContext(sc) 112 | """处理数据集,生成特征向量""" 113 | dfTitles = sqlContext.read.parquet('data/roll_news_sina_com_cn.parquet') 114 | print(dfTitles.dtypes) 115 | tokenizer = Tokenizer(inputCol="title", outputCol="words") 116 | wordsData = tokenizer.transform(dfTitles) 117 | hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20) 118 | featurizedData = hashingTF.transform(wordsData) 119 | idf = IDF(inputCol="rawFeatures", outputCol="features") 120 | idfModel = idf.fit(featurizedData) 121 | rescaledData = idfModel.transform(featurizedData) 122 | rescaledData.show() 123 | for features_label in rescaledData.select("features", "rawFeatures").take(3): 124 | print(features_label) 125 | """决策树模型培训""" 126 | labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(rescaledData) 127 | featureIndexer =\ 128 | VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(rescaledData) 129 | (trainingData, testData) = rescaledData.randomSplit([0.7, 0.3]) 130 | dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") 131 | pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt]) 132 | model = pipeline.fit(trainingData) 133 | """模型测试""" 134 | predictions = model.transform(testData) 135 | predictions.show() 136 | predictions.select("prediction", "indexedLabel", "features").show(5) 137 | """用户数据测试,单个新闻测试""" 138 | sentenceData = sqlContext.createDataFrame([ 139 | (label,title), 140 | ],['label',"title"]) 141 | tokenizer = Tokenizer(inputCol="title", outputCol="words") 142 | wordsData = tokenizer.transform(sentenceData) 143 | hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20) 144 | featurizedData = hashingTF.transform(wordsData) 145 | rescaledData = idfModel.transform(featurizedData) 146 | myprediction = model.transform(rescaledData) 147 | print("==================================================") 148 | myprediction.show() 149 | resultList = convertDfToList(myprediction) 150 | 151 | """模型评估""" 152 | evaluator = MulticlassClassificationEvaluator( 153 | labelCol="indexedLabel", predictionCol="prediction", metricName="precision") 154 | accuracy = evaluator.evaluate(predictions) 155 | print("Test Error = %g " % (1.0 - accuracy)) 156 | 157 | treeModel = model.stages[2] 158 | print(treeModel) 159 | 160 | sc.stop() 161 | return render(request,{'resultList':resultList}) 162 | 163 | def getSensitiveNews(request): 164 | """7.查询敏感信息""" 165 | conf = SparkConf().setAppName('textPredict').setMaster('spark://HP-Pavilion:7077') 166 | sc = SparkContext(conf=conf) 167 | sqlContext = SQLContext(sc) 168 | 169 | sc.stop() 170 | 171 | def predictTextHotDegree(request): 172 | """8.文本热度预测""" 173 | conf = SparkConf().setAppName('textPredict').setMaster('spark://HP-Pavilion:7077') 174 | sc = SparkContext(conf=conf) 175 | sqlContext = SQLContext(sc) 176 | 177 | sc.stop() 178 | 179 | 180 | 181 | 182 | 183 | -------------------------------------------------------------------------------- /system/views.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raymon-tian/networkPublicOpinionAnalysisSystem/b0ffed4415cb51a81a360aa0b65715a12dfe22ee/system/views.pyc -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | # #coding=utf-8 2 | # import re 3 | # from string import punctuation,digits,letters,whitespace 4 | # import sys 5 | # import myuntils 6 | # 7 | # """字符串过滤""" 8 | # temp = "想做/ 兼_职/学生_/ 的 、加,我Q: 1 5. 8 0. !!?? 8 6 。0. 2。 3 有,惊,喜,哦" 9 | # temp2 = temp.decode("utf8") 10 | # string = re.sub("[\s+\.\!\/_,$%^*(+\"\']+|[+——!,。?、~@#¥%……&*()]+".decode("utf8"), "".decode("utf8"),temp2) 11 | # string = string.encode('utf-8') 12 | # string.translate(None,punctuation) 13 | # string.translate(None,digits) 14 | # string.translate(None,letters) 15 | # string.translate(None,whitespace) 16 | # print(type(temp)) 17 | # print(type(string)) 18 | # print string 19 | # 20 | # string = '中国' 21 | # s1 = string.decode('utf-8') 22 | # print(type(s1)) 23 | # print(sys.getdefaultencoding()) 24 | # 25 | # string = '1111sfsdfs所发生的11 :南京航空航天大学: ,11sdfsdf王栋1111' 26 | # string = myuntils.handleAndCut(string) 27 | # print(type(string)) 28 | # string = string.encode('utf-8') 29 | # print(type(string)) 30 | # 31 | # l = ['111','222'] 32 | # print(l) 33 | from django.shortcuts import render 34 | from pyspark import SparkConf,SparkContext 35 | from pyspark.sql import SQLContext,Row 36 | from pyspark.ml.feature import HashingTF,IDF,Tokenizer 37 | from pyspark.ml import Pipeline 38 | from pyspark.ml.classification import DecisionTreeClassifier 39 | from pyspark.ml.feature import StringIndexer, VectorIndexer 40 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 41 | 42 | def convertDfToList(DataFrame): 43 | """将 DataFrame 转换为 List""" 44 | l = DataFrame.take(DataFrame.count()) 45 | myList = [] 46 | for row in l: 47 | dic = row.asDict() 48 | myList.append(dic) 49 | return myList 50 | 51 | """1.系统首页,显示各个数据源下前几条新闻""" 52 | conf = SparkConf().setAppName('index').setMaster('spark://HP-Pavilion:7077') 53 | sc = SparkContext(conf=conf) 54 | sqlContext = SQLContext(sc) 55 | rawNews = sc.textFile(name='data/roll_news_sina_com_cn.csv') 56 | parts = rawNews.map(lambda line:line.split(',')) 57 | titleNews = parts.map(lambda p:Row(label=p[0],title=p[1],url=p[2],time=p[3])) 58 | dfTitleNews = sqlContext.createDataFrame(titleNews) 59 | partions = dfTitleNews.orderBy('time',ascending=0).limit(10).select("*") 60 | sinaNewsList = convertDfToList(partions) 61 | print(sinaNewsList) 62 | sc.stop() -------------------------------------------------------------------------------- /tfidf.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from pyspark import SparkConf,SparkContext 3 | from pyspark.sql import SQLContext,Row,DataFrame 4 | from pyspark.ml.feature import HashingTF,IDF,Tokenizer 5 | 6 | conf = SparkConf().setAppName('tfidf').setMaster('spark://HP-Pavilion:7077') 7 | sc = SparkContext(conf=conf) 8 | sqlContext = SQLContext(sc) 9 | 10 | dfTitles = sqlContext.read.parquet('roll_news_sina_com_cn.parquet') 11 | print(dfTitles.dtypes) 12 | tokenizer = Tokenizer(inputCol="title", outputCol="words") 13 | wordsData = tokenizer.transform(dfTitles) 14 | hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20) 15 | featurizedData = hashingTF.transform(wordsData) 16 | idf = IDF(inputCol="rawFeatures", outputCol="features") 17 | idfModel = idf.fit(featurizedData) 18 | rescaledData = idfModel.transform(featurizedData) 19 | rescaledData.show() 20 | for features_label in rescaledData.select("features", "rawFeatures").take(3): 21 | print(features_label) 22 | 23 | sc.stop() 24 | -------------------------------------------------------------------------------- /项目.txt: 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