NLP15-基于keras的中文情感挖掘试探

摘要：keras开发，tf为后端；采用了两个样本（ChnSentiCorp_htl_ba_2000与imdb），三个神经网络的试探性运行（全连接的一般神经网络NN，LSTM，CNN），感觉keras比tf写代码更简单。对于NN只要参数充够的多，会拟合得很好，不过这样产生了过拟合；LSTM比CNN运行的效果好很多。
keras的中文文档： http://keras-cn.readthedocs.io/en/latest/

NN

下载数据
Downloading data from https://s3.amazonaws.com/text-datasets/imdb_full.pkl,运行imdb.load_data()将会下载。
这里面的数据，是一个整数矩阵，每个整数代表一个词，这里对每个词做了一个整型编码了。

# 探索一下数据情况
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
from keras.datasets import imdb
from keras.layers import Embedding,Flatten,Dense
from keras.models import Sequential
from keras.preprocessing import sequence

## EDA
# 加载数据，这个数据来自： https://s3.amazonaws.com/text-datasets/imdb_full.pkl
(x_train,y_train),(x_test,y_test) = imdb.load_data()
# 探索一下数据情况
lens = list(map(len,x_train))
avg_len = np.mean(lens)
print(avg_len)
plt.hist(lens,bins=range(min(lens),max(lens) + 50,50))
plt.show()
# 由于长度不同，这里取相同的长度
m = max(max(list(map(len,x_train))),max(list(map(len,x_test))))
print('m=%d' % m)
maxword = min(400,m)
x_train = sequence.pad_sequences(x_train,maxlen=maxword)
x_test = sequence.pad_sequences(x_test,maxlen=maxword)
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
#词数
vocab_siz = np.max([np.max(x_train[i]) for i in range(x_train.shape[0])]) + 1
print('vocab_siz=%d' % vocab_siz)
print('x_train.shape=[%d,%d]' % (x_train.shape[0],x_train.shape[1]))
#构建模型
model = Sequential()
# 第一层是嵌入层,矩阵为 vocab_siz * 64
model.add(Embedding(vocab_siz,64,input_length=maxword))
# 把矩阵压平，变成vocab_siz * 64维的向量
model.add(Flatten())
# 加入多层全连接
model.add(Dense(2000,activation='relu'))
model.add(Dense(500,activation='relu'))
model.add(Dense(200,activation='relu'))
model.add(Dense(50,activation='relu'))
# 最后一层输进0~1之间的值，像lr那样
model.add(Dense(1,activation='sigmoid'))
# 计算
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
print(model.summary())
print(type(x_train))
#训练
model.fit(x_train,y_train,validation_data=(x_test,y_test),batch_size=100,nb_epoch=20,verbose=1)
score = model.evaluate(x_test,y_test)
print(score)

控制台显示：

---

________________________
Layer (type) Output Shape Param # Connected to ====================================================================================================
embedding_1 (Embedding) (None,400,64) 5669568 embedding_input_1[0][0]          
____________________________________________________________________________________________________
flatten_1 (Flatten) (None,25600) 0 embedding_1[0][0]                
____________________________________________________________________________________________________
dense_1 (Dense) (None,2000) 51202000 flatten_1[0][0]                  
____________________________________________________________________________________________________
dense_2 (Dense) (None,500) 1000500 dense_1[0][0]                    
____________________________________________________________________________________________________
dense_3 (Dense) (None,200) 100200 dense_2[0][0]                    
____________________________________________________________________________________________________
dense_4 (Dense) (None,50) 10050 dense_3[0][0]                    
____________________________________________________________________________________________________
dense_5 (Dense) (None,1) 51 dense_4[0][0] ====================================================================================================
Total params: 57,982,369
Trainable params: 57,369
Non-trainable params: 0

Epoch 1/20
25000/25000 [==============================] - 452s - loss: 0.4248 - acc: 0.7779 - val_loss: 0.2961 - val_acc: 0.8768
Epoch 2/20
25000/25000 [==============================] - 458s - loss: 0.0779 - acc: 0.9730 - val_loss: 0.4230 - val_acc: 0.8503
Epoch 3/20
25000/25000 [==============================] - 450s - loss: 0.0050 - acc: 0.9985 - val_loss: 0.7284 - val_acc: 0.8522
Epoch 4/20
25000/25000 [==============================] - 452s - loss: 0.0031 - acc: 0.9990 - val_loss: 0.9187 - val_acc: 0.8420
Epoch 5/20
25000/25000 [==============================] - 449s - loss: 0.0052 - acc: 0.9982 - val_loss: 1.0336 - val_acc: 0.8362

LSTM

语料的样子，网上找到的情感分析语料“ChnSentiCorp_htl_ba_2000”

上成的例子中，显示出来的数据都是一整数，这个整数是词的id，上面把构建 id的过程省略了，这个LSTM的例子把这个词转id的过程补回来；同时，把对这个语料的处理的过程也补上，首先把pos与neg文件合并成一个文档，然后再把这两个合成一个文档，分词。
把这个分好词的文档运用Gensim构建词典，形成id与词的一个映射，转成以整数为id的向量矩阵。
探索词的维数据，选择合适的维度，如果长句进行截断，如果是短句进行填充。
处理完就可以构建LSTM了，训练，评估。。。。

代码：

# -*- coding:utf-8-*-

import os
import re
import numpy as np
import matplotlib.pyplot as plt
# 分词
from pprint import pprint

import jieba
from bs4 import BeautifulSoup
from gensim import corpora
from keras.layers import Embedding,LSTM,Dense,Activation,Dropout
from keras.models import Sequential
from keras.preprocessing import sequence
from sklearn.cross_validation import train_test_split


def cutPhase(inFile,outFile):
    # 如果没有自己定义的词典，这行不要
    # jieba.load_userdict("dict_all.txt")
    # 加载停用词
    stoplist = {}.fromkeys([line.strip() for line in open('data/stopword.txt','r',encoding='utf-8')])
    f1 = open(inFile,encoding='utf-8')
    f2 = open(outFile,'w+',encoding='utf-8')
    line = f1.readline()
    count = 0
    while line:
        b = BeautifulSoup(line,"lxml")
        line = b.text
        # 分词
        segs = jieba.cut(line,cut_all=False)
        # 过滤停用词
        segs = [word for word in list(segs)
                if word.lstrip() is not None
                and word.lstrip() not in stoplist
                ]
        # 每个词用空格隔开
        f2.write(" ".join(segs))
        f2.write('n')
        line = f1.readline()
        count += 1
        if count % 100 == 0:
            print(count)
    f1.close()
    f2.close()


def load_data(out_pos_name='data/pos.txt',out_neg_name='data/neg.txt'):
    def do_load(file_name,dir):
        c = 0
        with open(file_name,encoding='utf-8') as f_out:
            for root,_,files in os.walk(dir):
                # print(root)
                for f_name in files:
                    p = os.path.join(root,f_name)
                    try:
                        with open(p,mode='r',encoding='gbk') as f_read:
                            # print(os.path.join(root,f_name))
                            c += 1
                            txt = f_read.read()
                            txt = re.subn(r's+',' ',txt)[0]
                            f_out.write('%sn' % (txt))
                            # if c % 100 == 0:
                            # print(c)
                    except Exception as e:
                        print('p:',p)
                        # print('e:',e)

    print('加载pos!!!')
    do_load(out_pos_name,'data/ChnSentiCorp_htl_ba_2000/pos')
    print('加载neg!!!')
    do_load(out_neg_name,'data/ChnSentiCorp_htl_ba_2000/neg')


def combine_data():
    c = 0
    f_w = open('data/train.cut',encoding='utf-8')
    f_pos = open('data/pos.cut',encoding='utf-8')
    line = f_pos.readline()
    while line:
        c += 1
        f_w.write('%dt%s' % (1,line))
        line = f_pos.readline()
        print(c)
    f_pos.close()

    f_neg = open('data/neg.cut',encoding='utf-8')
    line = f_neg.readline()
    while line:
        c += 1
        f_w.write('%dt%s' % (0,line))
        line = f_neg.readline()
        print(c)
    f_neg.close()

    f_w.close()


if __name__ == '__main__':
    # print('# 加载数据')
    # load_data(out_pos_name='data/pos.txt',out_neg_name='data/neg.txt')
    # print('# 分词')
    # cutPhase(inFile='data/pos.txt',outFile='data/pos.cut')
    # cutPhase(inFile='data/neg.txt',outFile='data/neg.cut')
    # 数据融合
    # combine_data()
    Y = []
    x = []
    for line in open('data/train.cut',encoding='utf-8'):
        label,sentence = line.split("t")
        Y.append(int(label))
        x.append(sentence.split())

    print('#构建字典')
    dic = corpora.Dictionary(x)
    X = []
    for row in x:
        tmp = []
        for w_i in row:
            tmp.append(dic.token2id[w_i])
        X.append(tmp)
    X = np.array(X)
    Y = np.array(Y)
    # lens = list(map(len,X))
    # avg_len = np.mean(lens)
    # print(avg_len)
    # plt.hist(lens,bins=range(min(lens),max(lens) + 50,50))
    # plt.show()

    # 由于长度不同，这里取相同的长度,平均长度为38.18，最大长度为337.
    m = max(list(map(len,X)))
    print('m=%d' % m)
    maxword = min(100,m)
    X = sequence.pad_sequences(X,maxlen=maxword)
    print(X.shape)

    ## 数据划分
    x_train,x_test,y_test = train_test_split(X,Y,test_size=0.2,random_state=42)

    # 构建模型
    model = Sequential()
    model.add(Embedding(len(dic) + 1,128,input_length=maxword))
    # model.add(LSTM(128,dropout_W=0.2,return_sequences=True))
    # model.add(LSTM(64,return_sequences=True))
    model.add(LSTM(128,dropout_W=0.2))
    model.add(Dense(1))
    model.add(Activation("sigmoid"))
    # 计算
    model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"])
    print(model.summary())
    # 进行训练
    model.fit(x_train,nb_epoch=10,y_test))
    ## 结果评估
    score,acc = model.evaluate(x_test,y_test,batch_size=100)
    print("score: %.3f,accuracy: %.3f" % (score,acc))

    # # 预测
    # my_sentences = ['讨厌 房间']
    # my_x = []
    # for s in my_sentences:
    # words = s.split()
    # tmp = []
    # for w_j in words:
    # tmp.append(dic.token2id[w_j])
    # my_x.append(tmp)
    # my_X = np.array(my_x)
    # my_X = sequence.pad_sequences(my_X,maxlen=maxword)
    # labels = [int(round(x[0])) for x in model.predict(my_X)]
    # for i in range(len(my_sentences)):
    # print('%s:%s' % ('正面' if labels[i] == 1 else '负面',my_sentences[i]))

    # 这里面没有考虑到字典没有的词，这个作为下一个版本改进点。

LSTM模型：
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to ====================================================================================================
embedding_1 (Embedding) (None,100,64) 748800 embedding_input_1[0][0]          
____________________________________________________________________________________________________
lstm_1 (LSTM) (None,128) 98816 embedding_1[0][0]                
____________________________________________________________________________________________________
lstm_2 (LSTM) (None,64) 49408 lstm_1[0][0]                     
____________________________________________________________________________________________________
lstm_3 (LSTM) (None,32) 12416 lstm_2[0][0]                     
____________________________________________________________________________________________________
dense_1 (Dense) (None,1) 33 lstm_3[0][0]                     
____________________________________________________________________________________________________
activation_1 (Activation) (None,1) 0 dense_1[0][0] ====================================================================================================
Total params: 909,473
Trainable params: 909,473
score: 0.572,accuracy: 0.859

____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to ====================================================================================================
embedding_1 (Embedding) (None,128) 98816 embedding_1[0][0]                
____________________________________________________________________________________________________
dense_1 (Dense) (None,1) 129 lstm_1[0][0]                     
____________________________________________________________________________________________________
activation_1 (Activation) (None,1) 0 dense_1[0][0] ====================================================================================================
Total params: 847,745
Trainable params: 847,745
Non-trainable params: 0
score: 0.302,accuracy: 0.871

____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to ====================================================================================================
embedding_1 (Embedding) (None,128) 1497600 embedding_input_1[0][0]          
____________________________________________________________________________________________________
lstm_1 (LSTM) (None,128) 131584 embedding_1[0][0]                
____________________________________________________________________________________________________
dense_1 (Dense) (None,1) 0 dense_1[0][0] ====================================================================================================
Total params: 1,629,313
Trainable params: 1,313
Non-trainable params: 0
score: 0.386,accuracy: 0.874

CNN

对上面的LSTM模型结构考虑使用CNN，主要修改神经网络模型。

# 构建模型CNN
model = Sequential()
model.add(Embedding(len(dic) + 1,128,input_length=maxword))
model.add(Conv1D(nb_filter=128,filter_length=5,border_mode='same',activation='relu'))
model.add(MaxPooling1D(pool_length=2))
model.add(Dropout(0.25))
model.add(Conv1D(nb_filter=128,filter_length=3,activation='relu'))
model.add(MaxPooling1D(pool_length=2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64,activation='relu'))
model.add(Dense(32,activation='relu'))
model.add(Dense(1,activation='relu'))
# 计算
model.compile(loss="binary_crossentropy",optimizer="rmsprop",metrics=["accuracy"])
print(model.summary())
# 进行训练
model.fit(x_train,y_train,batch_size=100,nb_epoch=20,validation_data=(x_test,y_test))
## 结果评估
score,acc = model.evaluate(x_test,y_test,batch_size=100,verbose=1)
print("score: %.3f,accuracy: %.3f" % (score,acc))

____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to ====================================================================================================
embedding_1 (Embedding) (None,128) 1497600 embedding_input_1[0][0]          
____________________________________________________________________________________________________
convolution1d_1 (Convolution1D) (None,128) 82048 embedding_1[0][0]                
____________________________________________________________________________________________________
maxpooling1d_1 (MaxPooling1D) (None,50,128) 0 convolution1d_1[0][0]            
____________________________________________________________________________________________________
dropout_1 (Dropout) (None,128) 0 maxpooling1d_1[0][0]             
____________________________________________________________________________________________________
convolution1d_2 (Convolution1D) (None,128) 49280 dropout_1[0][0]                  
____________________________________________________________________________________________________
maxpooling1d_2 (MaxPooling1D) (None,25,128) 0 convolution1d_2[0][0]            
____________________________________________________________________________________________________
dropout_2 (Dropout) (None,128) 0 maxpooling1d_2[0][0]             
____________________________________________________________________________________________________
flatten_1 (Flatten) (None,3200) 0 dropout_2[0][0]                  
____________________________________________________________________________________________________
dense_1 (Dense) (None,64) 204864 flatten_1[0][0]                  
____________________________________________________________________________________________________
dense_2 (Dense) (None,32) 2080 dense_1[0][0]                    
____________________________________________________________________________________________________
dense_3 (Dense) (None,1) 33 dense_2[0][0] ====================================================================================================
Total params: 1,835,905
Trainable params: 1,905
Non-trainable params: 0
____________________________________________________________________________________________________

这里主是为了测试，效果不怎么样。没有前面的LSTM好，考虑研究改进，或考虑学习CNNText…

[happyprince,http://blog.csdn.net/ld326/article/details/78670821]