ChineseClassification/main.py at main · Puteto/ChineseClassification · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
# ~~~~~~~~~~~~~~~~~~~~
# Code by Anleo YUAN
# Please visit https://www.anleo.top
# Copyrights reserved by Chongqing University
# ~~~~~~~~~~~~~~~~~~~~
from tkinter import *
from tkinter import messagebox
import jieba
import jieba.posseg as pseg
import math
from array import array
import numpy as np
import scipy
from scipy import sparse
from libsvm.svmutil import *
from libsvm.svm import *
from libsvm.commonutil import *
from testFeatureWeight import *

# 导入文件
classCode = [ '财经','房产','股票','家居','教育','科技','社会','时尚','时政','体育','游戏','娱乐' ]
emotionCode = [ '褒义的', '贬义的']
classFeature = "classFeature.txt"
emotionFeature = "emotionFeature.txt"
classDfFeature = "classDfFeature.txt"
emotionDfFeature = "emotionDfFeature.txt"
classTrainData = "classTrainData.svm"
emotionTrainData = "emotionTrainData.svm"
classModel = "classTrain.model"
emotionModel = "emotionTrain.model"
classParam = "classParam.svm"
emotionParam = "emotionParam.svm"
trainDocumentCount = 60000

# 交互GUI设计
window = Tk()
window.title('中文文本分类和情感分析系统')
window.geometry('465x500')

lbl1 = Label(window, text="中文文本分类与情感分析",font=('黑体', 20))
lbl1.grid(row=0, column=0, columnspan=4, pady=10)

lbl2= Label(window, text="请输入文本:",font=('楷体', 8))
lbl2.grid(row=2, column=0, padx=20, pady=10)

txt = Text(window, width=60, height=13, font=('宋体', 10), relief=FLAT)
txt.grid(row=3, column=0, columnspan=4, padx=20)

# 将输入的文本分词处理
def textSeg(content):
    words = pseg.cut(content) # 分词
    finalContent = []
    # 停用词列表
    stopWords = [line.strip() for line in open('Chinesestopword.txt', 'r', encoding='utf-8').readlines()]

    for word in words:
        word = str(word.word)
        # 如果该单词非空格、换行符、不在听用词表中就将其添加进入最终分词列表中
        if len(word) > 1 and word != '\n' and word != '\u3000' and word not in stopWords:
            finalContent.append(word)

    # 组合成最终需要的字符串
    finalStr = " ".join(finalContent)
    finalFeature = finalStr.split(' ')
    return finalFeature

# 读取缩放范围
def readParam(paramName):
    paramFile = open(paramName, 'r', encoding='utf-8')
    paramContent = paramFile.read().split("\n")
    paramFile.close()
    xParam = []
    for eachParam in paramContent:
        eachParam = eachParam.split(" ")
        if len(eachParam) == 3:
            xParam.append(eachParam[2])
    return xParam

# 将输入文本特征转为TF-IDF值，并缩放存为libsvm格式
def featureToData(testFeature, featureName, dfFeatureName, paramName):
    feature = readFeature(featureName)
    dfFeatureDic = readDfFeature(dfFeatureName)
    xParam = readParam(paramName)
    prob_x = array('d')
    row_ptr = array('l', [0])
    col_idx = array('l')
    nz = 0
    for i in range(len(feature)):
        if feature[i] in testFeature:
            curFeature = feature[i]
            featureCount = testFeature.count(feature[i])
            tf = float(featureCount)/(len(testFeature))
            idfFeature = math.log(float(trainDocumentCount+1)/(int(dfFeatureDic[curFeature])+2))
            featureValue = tf * idfFeature
            featureValue = float(featureValue)/float(xParam[i])
            col_idx.append(int(i))
            prob_x.append(featureValue)
            nz += 1
    if nz == 0:
        return nz
    row_ptr.append(row_ptr[-1]+nz)
    prob_x = np.frombuffer(prob_x, dtype='d')
    col_idx = np.frombuffer(col_idx, dtype='l')
    row_ptr = np.frombuffer(row_ptr, dtype='l')
    prob_x = sparse.csr_matrix((prob_x, col_idx, row_ptr))
    return prob_x

# 预测函数
def testData(xData, modelName):
    model = svm_load_model(modelName)
    yt = array('d')
    yt = np.frombuffer(yt, dtype='d')
    p_lable, p_acc, p_val = svm_predict(yt, xData, model)
    return p_lable, p_acc, p_val

def classed():
    content = txt.get('1.0', 'end')
    entryFeature = textSeg(content)
    x = featureToData(entryFeature, classFeature, classDfFeature, classParam)
    if isinstance(x, int):
        messagebox.showerror("文本分类", '很抱歉：\n当前模型无法预测')
    else:
        p_lable, p_acc, p_val = testData(x, classModel)
        print(p_lable)
        result = classCode[int(p_lable[0])]
        messagebox.showinfo("文本分类", '预测结果：\n ' + str(result) + " 类文本")#占位
    content = ""

def emotion():
    content = txt.get('1.0', 'end')
    entryFeature = textSeg(content)
    x = featureToData(entryFeature, emotionFeature, emotionDfFeature, emotionParam)
    if isinstance(x, int):
        messagebox.showerror("情感分析", '很抱歉：\n当前模型无法预测')
    else:
        p_lable, p_acc, p_val = testData(x, emotionModel)
        print(p_lable)
        result = emotionCode[int(p_lable[0])]
        messagebox.showinfo("情感分析", '预测结果：\n ' + str(result))  # 占位
    content = ""

def delete():
    txt.delete('1.0', 'end')


btnclass = Button(window, text="文本分类", command=classed, font=('思源黑体 CN Medium', 12))
btnclass.grid(row=1,column=1,padx=10, ipadx=5)

btnemotion = Button(window, text="情感分析", command=emotion, font=('思源黑体 CN Medium', 12))
btnemotion.grid(row=1, column=2, padx=10, ipadx=5)

btndelete = Button(window, text="清空", command=delete)
btndelete.grid(row=4, column=3, padx=20, pady=10, ipadx=10)

lbl3= Label(window, text="2023 © CQU",font=('思源宋体 CN Medium', 8))
lbl3.grid(row=5, column=0, padx=20, pady=10)

window.mainloop()