本科毕设
来源：互联网 发布：龙神契约进阶数据 编辑：程序博客网 时间：2024/04/27 17:13
第一篇博文，算是正式开始学习编程。
介绍下自己的本科毕。
题目：网络新闻标签与民众情绪关联性分析及其软件设计开发
使用的是python语言，对在datatang获得的新闻语料进行处理分析。
首先是新闻语料的读取，一共49000条新闻及对应的新闻情绪投票：
就python文件流依次读取txt文件，字符串处理去掉空格之类的字符，再保存到txt，代码如下
# -*- coding: utf-8 -*-' txt to txt module 'import stringimport sys__author__ = 'Lust 'reload(sys)sys.setdefaultencoding('utf-8')count = 0if __name__ == '__main__': # for x in range(1,49001): wr = open("b.txt", 'w') for x in range(1, 5000): f = open(r'C:\Users\Administrator\data\%s.txt' % x, 'r') a = f.readlines() b1 = a[0].decode('utf-8', 'ignore') b2 = a[-2].decode('utf-8', 'ignore') b3 = a[-1].decode('utf-8', 'ignore') b1 = b1.replace(' ', '') b2 = b2.replace(' ', '') b3 = b3.replace(' ', '') b1 = b1.replace('#title#', '') b2 = b2.replace('#emotion#', '') b3 = b3.replace('#emotion_sum#', '') b1 = b1.replace('#/title#', '') b2 = b2.replace('#/emotion#', '') b3 = b3.replace('#/emotion_sum#', '') b3 = b3.replace(u'共有', '') b3 = b3.replace(u'投票', '') if(string.atoi(b3)) > 100: wr.write(b1) wr.write(b3) wr.write('\n') wr.write(b2) count = count + 1 print 'count', count # print x f.close() wr.close()另外还把提取的信息存到了excel里，但是并没有什么卵用，excel存的东西越多执行起来越慢，用的是xlrd和xlutils两个包。
下一步，对新闻进行分词，提取关键字，直接用的jieba分词包，代码如下
# coding=utf-8import jieba.analyseimport sysimport pickle_author__ = 'Lust'reload(sys)sys.setdefaultencoding('utf-8')f = open('topic.txt', 'r')a = f.readlines()l = []for i in range(0, 5495): seg = jieba.analyse.extract_tags(a[i]) l.append(seg)f.close()f = open('keywords.pkl', 'w')pickle.dump(l, f)f.close()自然语言处理里面nltk据说也很好用，因为教程基本是对英文，就试了一下放弃了。这个过程中还把每条新闻对应关键字出现情况的矩阵转换成了MATLAB中的.mat文件，也没什么卵用，就是用下scipy.io
新闻内容处理里面还用了点正则表达式，就是python中的re
temp = re.compile(r'#comment#(.+)#/comment#') comment_temp = temp.findall(txt) if comment_temp == []: comment_temp.append(title_temp) comment_temp = comment_temp[0]
大概就是这么个意思。
根据新闻关键词的共现关系建立网络，并对网络进行分析和可视化。复杂网络分析及可视化用的是python中networkx，发现网络中的社团结构并可视化的代码如下：
import sysimport cPickle as pickleimport matplotlib.pyplot as pltimport networkx as nximport jieba.analyseimport random__author__ = 'Lust 'reload(sys)sys.setdefaultencoding('utf-8')def b(): f = open( r'C:\Users\Administrator\Desktop\python\graduation_project\network\keywords_freq_dict.pkl', 'r') keywords_freq_dict = pickle.load(f) f.close() f = open( r'C:\Users\Administrator\Desktop\python\graduation_project\network\keywords_idf_dict.pkl', 'r') keywords_idf_dict = pickle.load(f) f.close() f = open( r'C:\Users\Administrator\Desktop\python\graduation_project\network\keywords_value_idf_content.pkl', 'r') keywords_value_idf_content = pickle.load(f) f.close() f = open( r'C:\Users\Administrator\Desktop\python\graduation_project\network\label_emotion.pkl', 'r') label_emotion = pickle.load(f) f.close() G = nx.Graph() H = nx.Graph() # threshold = 1.3 threshold = 0.6 sample = 5494 s = u'警方' count = 0 for x in xrange(0, sample): if label_emotion[x][8] == False: count = count + 1 for keywords_len_1 in xrange(len(keywords_value_idf_content[x])): for keywords_len_2 in xrange(len(keywords_value_idf_content[x])): # if (keywords_len_1 != keywords_len_2) and (keywords_value_idf_content[x][keywords_len_1][1] + keywords_value_idf_content[x][keywords_len_2][1] > 0): if (keywords_len_1 != keywords_len_2): # G.add_node(keywords_value_idf_content[x][keywords_len_1][0], IDF=keywords_idf_dict[keywords_value_idf_content[x][keywords_len_1][0]]) # if keywords_value_idf_content[x][keywords_len_1][0] == s: # print s, keywords_value_idf_content[x][keywords_len_1][2], keywords_value_idf_content[x][keywords_len_1][3] # G.add_node(keywords_value_idf_content[x][keywords_len_2][0], IDF=keywords_idf_dict[keywords_value_idf_content[x][keywords_len_2][0]]) # if keywords_value_idf_content[x][keywords_len_2][0] == s: # print s, keywords_value_idf_content[x][keywords_len_2][2], keywords_value_idf_content[x][keywords_len_2][3] G.add_edge(keywords_value_idf_content[x][keywords_len_1][0], keywords_value_idf_content[x][keywords_len_2][0], weight=0) # G.add_edge(keywords_1[0], keywords_2[0]) # weight_temp = G.get_edge_data(keywords_1[0], keywords_2[0]) # if(weight_temp == {}): # G[keywords_1[0]][keywords_2[0]]['weight'] = keywords_1[1] + keywords_2[1] # print 'first:', keywords_1[0], keywords_2[0], G[keywords_1[0]][keywords_2[0]]['weight'] # else: # G[keywords_1[0]][keywords_2[0]]['weight'] = keywords_1[1] + keywords_2[1] + weight_temp['weight'] # print 'second:', keywords_1[0], keywords_2[0], weight_temp['weight'] # print x # return for x in xrange(0, sample): for keywords_1 in keywords_value_idf_content[x]: for keywords_2 in keywords_value_idf_content[x]: if (keywords_1 != keywords_2) and ((keywords_1[0], keywords_2[0]) in G.edges()): weight_temp = G.get_edge_data(keywords_1[0], keywords_2[0])['weight'] G[keywords_1[0]][keywords_2[0]]['weight'] = keywords_1[1] + keywords_2[1] + weight_temp # if keywords_1[0] == s or keywords_2[0] == s: # print keywords_1[0], keywords_2[0], G[keywords_1[0]][keywords_2[0]]['weight'] # print weight_temp, keywords_1[0], keywords_2[0], G[keywords_1[0]][keywords_2[0]]['weight'] print x # 穷举赋予权值 edgewidth = [] for (u, v, d) in G.edges(data=True): if d['weight'] >= threshold: edgewidth.append(round((d['weight']-threshold)*10, 2)) H.add_node(u, IDF=keywords_idf_dict[u]) H.add_node(v, IDF=keywords_idf_dict[v]) H.add_edge(u, v, weight=d['weight']) # c = list(nx.k_clique_communities(G, 2)) # G = nx.create_empty_copy(G) # pos = nx.graphviz_layout(G) pos = nx.spring_layout(H, iterations=20) # nodecolor = [] # for i in xrange(nx.number_of_nodes(G)): # nodecolor.append(random.random()) # nodecolor = [float(G.degree(v)) for v in G] IDF = dict.fromkeys(H.nodes(), 0.0) for (u, v) in H.nodes(data=True): IDF[u] = v['IDF'] nodesize = [IDF[v]*100 for v in H] b = list(nx.k_clique_communities(H, 3)) temp = [] for i in b: for j in i: temp.append(j) nodesize_2 = [] for i in temp: nodesize_2.append(IDF[i]*100) nx.draw_networkx_edges(H, pos, alpha=0.3, width=edgewidth, edge_color='m') nx.draw_networkx_edges(H, pos, alpha=0.4, width=1, edge_color='k') # nx.draw_networkx_nodes(G, pos, alpha=0.4, node_size=nodesize, node_color=nodecolor, vmin=0.0, vmax=1.0,) nx.draw_networkx_nodes(H, pos, alpha=0.4, node_size=nodesize, node_color='w') nx.draw_networkx_nodes(H, pos, alpha=0.4, nodelist=temp, node_size=nodesize_2, node_color='r') nx.draw_networkx_labels(H, pos, fontsize=14) # plt.axis('off') plt.show() for i in b: for j in i: print j # print G.number_of_edges() return G, b, count搞出来的图就是这样的：
红的就点是社团结构，边的宽度就是两个关键词联系紧密程度，节点大小代表词本身的信息量。最后把关键词作为特征训练分类器，对新闻情绪情绪进行分类，用python中的sklearn
from sklearn import svmfrom sklearn import cross_validationfrom sklearn import decompositiondef c(): f = open('C:\Users\Administrator\Desktop\python\graduation_project\wrapper\label.pkl', 'r') label = pickle.load(f) f.close() f = open('C:\Users\Administrator\Desktop\python\graduation_project\wrapper\data.pkl', 'r') data = pickle.load(f) f.close() print data.shape, label.shape label = label.reshape(-1) print data.shape, label.shape # perm = np.random.permutation(label.size) # data = data[perm] # label = label[perm] pca = decomposition.PCA(n_components=200) pca.fit(data) data = pca.transform(data) x_train, x_test, y_train, y_test = cross_validation.train_test_split(data, label, test_size=0.5, random_state=0) clf = svm.SVC(kernel='linear') clf.fit(x_train, y_train) score = clf.score(x_test, y_test) print score return data, label对特征矩阵进行了主成分分析PCA，sklearn里面的交叉验证cross_validation我用来分测试集训练集，用的svm分类器换各种各样的核。结果用matplotlib画图
最后的最后，用pyqt做了个交互界面
具体pyqt的用法全是学的这个http://www.cnblogs.com/rrxc/p/4462890.html
这个gui的代码如下：
# -*- coding: utf-8 -*-import sysfrom PyQt4 import QtCore, QtGui, uicimport cPickle as pickleimport randomimport matplotlib.pyplot as pltimport networkx as nxfrom matplotlib.backends import qt4_compatfrom matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvasfrom matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as NavigationToolbarfrom matplotlib.figure import Figurefrom sklearn import svmfrom sklearn import cross_validationfrom sklearn import decompositionqtCreatorFile = "C:\Users\Administrator\Downloads\gui.ui" # Enter file here.Ui_MainWindow, QtBaseClass = uic.loadUiType(qtCreatorFile)class MyApp(QtGui.QMainWindow, Ui_MainWindow): def __init__(self): f = open( r'C:\Users\Administrator\Desktop\python\graduation_project\statistics\data\two_tuple.pkl', 'r') global two_tuple two_tuple = pickle.load(f) f.close() f = open( r'C:\Users\Administrator\Desktop\python\graduation_project\network\keywords_idf_dict.pkl', 'r') global keywords_idf_dict keywords_idf_dict = pickle.load(f) f.close() f = open( r'C:\Users\Administrator\Desktop\python\graduation_project\network\keywords_value_idf_content.pkl', 'r') global keywords_value_idf_content keywords_value_idf_content = pickle.load(f) f.close() f = open( r'C:\Users\Administrator\Desktop\python\graduation_project\network\label_emotion.pkl', 'r') global label_emotion label_emotion = pickle.load(f) f.close() f = open('C:\Users\Administrator\Desktop\python\graduation_project\wrapper\label.pkl', 'r') global label_2 label_2 = pickle.load(f) label_2 = label_2.reshape(-1) f.close() f = open('C:\Users\Administrator\Desktop\python\graduation_project\wrapper\data.pkl', 'r') global data_2 data_2 = pickle.load(f) f.close() QtGui.QMainWindow.__init__(self) Ui_MainWindow.__init__(self) self.setupUi(self) self.compute_button.clicked.connect(self.Compute) self.plt_button.clicked.connect(self.Plt) self.two_tuple_button.clicked.connect(self.Two_tuple) self.pretreatment_button.clicked.connect(self.Pretreatment) self.predict_button.clicked.connect(self.Predict) # self.setWindowTitle('Demo: PyQt with matplotlib') self.setWindowTitle(u'标签组合提取与情绪预测') self.create_menu() self.create_main_frame() self.create_status_bar() self.textbox.setText('1 2 3') self.on_draw() def create_main_frame(self): # self.main_frame = QtGui.QWidget() # Create the mpl Figure and FigCanvas objects. # 5x4 inches, 100 dots-per-inch # self.dpi = 80 self.fig = Figure((9.0, 5.0), dpi=self.dpi) self.canvas = FigureCanvas(self.fig) self.canvas.setParent(self.main_frame) # Since we have only one plot, we can use add_axes # instead of add_subplot, but then the subplot # configuration tool in the navigation toolbar wouldn't # work. # # self.axes = self.fig.add_subplot(111) self.axes = self.fig.add_axes([0.1, 0.1, 0.8, 0.8]) # Bind the 'pick' event for clicking on one of the bars # self.canvas.mpl_connect('pick_event', self.on_pick) # Create the navigation toolbar, tied to the canvas # # self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame) # Other GUI controls # # self.textbox = QtGui.QLineEdit() self.textbox.setMinimumWidth(200) self.connect(self.textbox, QtCore.SIGNAL('editingFinished ()'), self.on_draw) # self.draw_button = QtGui.QPushButton("&Draw") self.connect(self.draw_button, QtCore.SIGNAL('clicked()'), self.on_draw) # self.grid_cb = QtGui.QCheckBox("Show &Grid") self.grid_cb.setChecked(False) self.connect(self.grid_cb, QtCore.SIGNAL('stateChanged(int)'), self.on_draw) # slider_label = QtGui.QLabel('Bar width (%):') # self.slider = QtGui.QSlider(QtCore.Qt.Horizontal) self.slider.setRange(1, 100) self.slider.setValue(20) self.slider.setTracking(True) self.slider.setTickPosition(QtGui.QSlider.TicksBothSides) self.connect(self.slider, QtCore.SIGNAL('valueChanged(int)'), self.on_draw) # # Layout with box sizers # # hbox = QtGui.QHBoxLayout() # for w in [self.textbox, self.draw_button, self.grid_cb, # slider_label, self.slider]: # hbox.addWidget(w) # hbox.setAlignment(w, QtCore.Qt.AlignVCenter) # vbox = QtGui.QVBoxLayout() # vbox.addWidget(self.canvas) # vbox.addWidget(self.mpl_toolbar) # vbox.addLayout(hbox) # self.main_frame.setLayout(vbox) # self.setCentralWidget(self.main_frame) def on_draw(self): """ Redraws the figure """ str = unicode(self.textbox.text()) self.data = map(int, str.split()) x = range(len(self.data)) # clear the axes and redraw the plot anew # self.axes.clear() self.axes.grid(self.grid_cb.isChecked()) self.axes.bar( left=x, height=self.data, width=self.slider.value() / 100.0, align='center', alpha=0.44, picker=5) # G = nx.star_graph(20) # pos = nx.spring_layout(G) # colors = range(20) # nx.draw(G, pos, node_color='#A0CBE2', edge_color=colors, width=4, edge_cmap=plt.cm.Blues, with_labels=False) # plt.savefig("atlas.png", dpi=50) # plt.show() self.canvas.draw() def on_pick(self, event): # The event received here is of the type # matplotlib.backend_bases.PickEvent # # It carries lots of information, of which we're using # only a small amount here. # box_points = event.artist.get_bbox().get_points() msg = "You've clicked on a bar with coords:\n %s" % box_points QtGui.QMessageBox.information(self, "Click!", msg) def create_menu(self): self.file_menu = self.menuBar().addMenu("&File") quit_action = self.create_action("&Quit", slot=self.close, shortcut="Ctrl+Q", tip="Close the application") self.add_actions(self.file_menu, (None, quit_action)) self.help_menu = self.menuBar().addMenu("&Help") about_action = self.create_action("&About", shortcut='F1', slot=self.on_about, tip='About the demo') self.add_actions(self.help_menu, (about_action,)) def create_action(self, text, slot=None, shortcut=None, icon=None, tip=None, checkable=False, signal="triggered()"): action = QtGui.QAction(text, self) if icon is not None: action.setIcon(QtGui.QIcon(":/%s.png" % icon)) if shortcut is not None: action.setShortcut(shortcut) if tip is not None: action.setToolTip(tip) action.setStatusTip(tip) if slot is not None: self.connect(action, QtCore.SIGNAL(signal), slot) if checkable: action.setCheckable(True) return action def add_actions(self, target, actions): for action in actions: if action is None: target.addSeparator() else: target.addAction(action) def on_about(self): msg = u""" 李~欣~桐~的demo程序: * 可以画图！ * Add values to the text box and press Enter (or click "Draw") * Show or hide the grid * Drag the slider to modify the width of the bars * Save the plot to a file using the File menu * Click on a bar to receive an informative message """ QtGui.QMessageBox.about(self, "About the demo", msg.strip()) def create_status_bar(self): self.status_text = QtGui.QLabel(u"这是李~欣~桐~的程序状态信息") self.statusBar().addWidget(self.status_text, 1) def Compute(self): f = open( r'C:\Users\Administrator\Desktop\python\graduation_project\network\title.pkl', 'r') title = pickle.load(f) f.close() a = title[random.randint(1, 20)] b = self.topic.toPlainText() total_price_string = "The total price with tax is: " + a[2] + '\n' + a[1] + b self.accuracy.setText(total_price_string) self.textbox.setText('4 5 6') def Plt(self): # G = nx.star_graph(20) # pos = nx.spring_layout(G) # colors = range(20) # nx.draw(G, pos, node_color='#A0CBE2', edge_color=colors, width=4, edge_cmap=plt.cm.Blues, with_labels=False) # plt.show() # f = open( # r'C:\Users\Administrator\Desktop\python\graduation_project\network\keywords_idf_dict.pkl', 'r') # keywords_idf_dict = pickle.load(f) # f.close() # f = open( # r'C:\Users\Administrator\Desktop\python\graduation_project\network\keywords_value_idf_content.pkl', 'r') # keywords_value_idf_content = pickle.load(f) # f.close() # f = open( # r'C:\Users\Administrator\Desktop\python\graduation_project\network\label_emotion.pkl', 'r') # label_emotion = pickle.load(f) # f.close() G = nx.Graph() # threshold = 1.3 threshold = 1.5 sample = 5 # s = u'警方' count = 0 for x in xrange(0, sample): if label_emotion[x][8] == False: count = count + 1 for keywords_len_1 in xrange(len(keywords_value_idf_content[x])): for keywords_len_2 in xrange(len(keywords_value_idf_content[x])): if (keywords_len_1 != keywords_len_2) and (keywords_value_idf_content[x][keywords_len_1][1] + keywords_value_idf_content[x][keywords_len_2][1] > 0): # if (keywords_len_1 != keywords_len_2): G.add_node(keywords_value_idf_content[x][keywords_len_1][0], IDF=keywords_idf_dict[keywords_value_idf_content[x][keywords_len_1][0]]) # if keywords_value_idf_content[x][keywords_len_1][0] == s: # print s, keywords_value_idf_content[x][keywords_len_1][2], keywords_value_idf_content[x][keywords_len_1][3] G.add_node(keywords_value_idf_content[x][keywords_len_2][0], IDF=keywords_idf_dict[keywords_value_idf_content[x][keywords_len_2][0]]) # if keywords_value_idf_content[x][keywords_len_2][0] == s: # print s, keywords_value_idf_content[x][keywords_len_2][2], keywords_value_idf_content[x][keywords_len_2][3] G.add_edge(keywords_value_idf_content[x][keywords_len_1][0], keywords_value_idf_content[x][keywords_len_2][0], weight=0) # G.add_edge(keywords_1[0], keywords_2[0]) # weight_temp = G.get_edge_data(keywords_1[0], keywords_2[0]) # if(weight_temp == {}): # G[keywords_1[0]][keywords_2[0]]['weight'] = keywords_1[1] + keywords_2[1] # print 'first:', keywords_1[0], keywords_2[0], G[keywords_1[0]][keywords_2[0]]['weight'] # else: # G[keywords_1[0]][keywords_2[0]]['weight'] = keywords_1[1] + keywords_2[1] + weight_temp['weight'] # print 'second:', keywords_1[0], keywords_2[0], weight_temp['weight'] # print x # return for x in xrange(0, sample): for keywords_1 in keywords_value_idf_content[x]: for keywords_2 in keywords_value_idf_content[x]: if (keywords_1 != keywords_2) and ((keywords_1[0], keywords_2[0]) in G.edges()): weight_temp = G.get_edge_data(keywords_1[0], keywords_2[0])['weight'] G[keywords_1[0]][keywords_2[0]]['weight'] = keywords_1[1] + keywords_2[1] + weight_temp # if keywords_1[0] == s or keywords_2[0] == s: # print keywords_1[0], keywords_2[0], G[keywords_1[0]][keywords_2[0]]['weight'] # print weight_temp, keywords_1[0], keywords_2[0], G[keywords_1[0]][keywords_2[0]]['weight'] # print x # 穷举赋予权值 edgewidth = [] for (u, v, d) in G.edges(data=True): edgewidth.append(round((d['weight']-threshold)*10, 2)) # G = nx.create_empty_copy(G) # pos = nx.graphviz_layout(G) pos = nx.spring_layout(G, iterations=20) # nodecolor = [] # for i in xrange(nx.number_of_nodes(G)): # nodecolor.append(random.random()) # nodecolor = [float(G.degree(v)) for v in G] IDF = dict.fromkeys(G.nodes(), 0.0) for (u, v) in G.nodes(data=True): IDF[u] = v['IDF'] nodesize = [IDF[v]*100 for v in G] b = list(nx.k_clique_communities(G, 3)) temp = [] for i in b: for j in i: temp.append(j) nodesize_2 = [] for i in temp: nodesize_2.append(IDF[i]*100) nx.draw_networkx_edges(G, pos, alpha=0.3, width=edgewidth, edge_color='m') nx.draw_networkx_edges(G, pos, alpha=0.4, width=1, edge_color='k') # nx.draw_networkx_nodes(G, pos, alpha=0.4, node_size=nodesize, node_color=nodecolor, vmin=0.0, vmax=1.0,) nx.draw_networkx_nodes(G, pos, alpha=0.4, node_size=nodesize, node_color='w') nx.draw_networkx_nodes(G, pos, alpha=0.4, nodelist=temp, node_size=nodesize_2, node_color='r') nx.draw_networkx_labels(G, pos, fontsize=14) # plt.axis('off') plt.show() def Two_tuple(self): # f = open( # r'C:\Users\Administrator\Desktop\python\graduation_project\statistics\data\two_tuple.pkl', 'r') # two_tuple = pickle.load(f) # f.close() a = two_tuple[random.randint(1, 20)] total_price_string = a[0] + '\n' + a[1] self.two_tuple_txt.setText(total_price_string) self.textbox.setText('4 5 6') def Pretreatment(self): a = self.address.toPlainText() total_price_string = a self.accuracy.setText(total_price_string) s = '%s 5 6' % len(data_2) self.textbox.setText(s) def Predict(self): data_3 = data_2 label_3 = label_2 pca = decomposition.PCA(n_components=200) pca.fit(data_3) data_3 = pca.transform(data_3) x_train, x_test, y_train, y_test = cross_validation.train_test_split(data_3, label_3, test_size=0.5, random_state=0) clf = svm.SVC(kernel='linear') clf.fit(x_train, y_train) score = clf.score(x_test, y_test) self.predict_accuracy.setText(u'%s' % score)if __name__ == "__main__": app = QtGui.QApplication(sys.argv) window = MyApp() window.show() sys.exit(app.exec_())PS:拖延症啊拖延症，拖的我都忘了毕设怎么做的了·····潦草的写了下，见谅
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