基于Attention Model的Aspect level文本情感分类---用Python+Keras实现

1、关于aspect level的情感分析

给定一个句子和句子中出现的某个aspect，aspect-level 情感分析的目标是分析出这个句子在给定aspect上的情感倾向。

例如：great food but the service was dreadful! 在aspect “food”上，情感倾向为正，在aspect “service”上情感倾向为负。Aspect level的情感分析相对于document level来说粒度更细。

2、关于attention model

使用传统的神经网络模型能够捕捉背景信息，但是不能明确的区分对某个aspect更重要的上下文信息。为了解决这个问题，引入attention捕获对于判断不同aspect的情感倾向较重要的信息。

3、模型的示意图

• 模型包括多个computational layers,每个computational layer包括一个attention layer和一个linear layer。
• 第一个computational layer，attention layer的输入是aspect vector，输出memory中的比较重要的部分，linear layer的输入是aspect vector。第一个computational layer的attention layer和linear layer的输出结果求和作为下一个computational layer的输入。
• 其它computational layer执行同样的操作，上一层的输出作为输入，通过attention机制获取memory中较重要的信息，与线性层得到的结果求和作为下一层的输入。
• 最后一层的输出作为结合aspect信息的sentence representation，作为aspect-level情感分类的特征，送到softmax。

4、深度学习框架Keras

Keras是一个高层神经网络库，Keras由纯Python编写而成并基Tensorflow或Theano。Keras 为支持快速实验而生，能够把idea迅速转换为结果。

具体可查看官方的中文文档写的很详细。

5、大概流程

读取数据集 –> 分词 –> 将所有词排序并标号 –> 词嵌入 –> Attention Model –> 编译 –> 训练+测试

6、参数设置

• 句子最大长度：80
• 词向量维度：300
• 梯度下降batch：32
• 总迭代次数： 5

---

实验代码：

# -*- coding: utf-8 -*-
import csv
import jieba
jieba.load_userdict('wordDict.txt')
import pandas as pd
from keras.preprocessing import sequence
from keras.utils import np_utils
from keras.models import *
from keras.optimizers import *
from keras.layers.core import *
from keras.layers import Input,merge, TimeDistributed
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.embeddings import Embedding
from keras.regularizers import l2
from keras import backend as K

np.random.seed(1337)


# 读取训练集
def readtrain():
    with open('allTrain_includeView.csv', 'rb') as csvfile:
        reader = csv.reader(csvfile)
        column1 = [row for row in reader]
    content_train = [i[1] for i in column1[1:]]
    view_train = [i[2] for i in column1[1:]]
    opinion_train = [i[3] for i in column1[1:]]
    print '训练集有 %s 条句子' % len(content_train)
    train = [content_train, view_train, opinion_train]
    return train


# 将utf8的列表转换成unicode
def changeListCode(b):
    a = []
    for i in b:
        a.append(i.decode('utf8'))
    return a


# 对列表进行分词用逗号连接
def segmentWord2(cont):
    c = []
    for i in cont:
        a = list(jieba.cut(i))
        c.append(a)
    return c


def transLabel(labels):
    for i in range(len(labels)):
        if labels[i] == 'pos':
            labels[i] = 2
        elif labels[i] == 'neu':
            labels[i] = 1
        elif labels[i] == 'neg':
            labels[i] = 0
        else: print "label无效：",labels[i]
    return labels


# content = ["我 来到 北京 清华大学", "他 来到 了 网易 杭研 大厦"]  sklearn输入格式
# content = [['我','来到', '北京'], ['他','来到','了']]  keras输入格式
train = readtrain()

content = segmentWord2(train[0])  #所有的词一起分词，包括训练集和预测集
view = changeListCode(train[1])
opinion = transLabel(train[2])



w = []  # 将所有词语整合在一起
for i in content:
    w.extend(i)
for i in view:  # 把view的词也加入进去
    w.append(i)



def get_aspect(X):
    ans = X[:, 0, :]
    return ans

def get_context(X):
    ans = X[:, 1:, :]
    return ans

def get_R(X):
    Y, alpha = X[0], X[1]
    ans = K.T.batched_dot(Y, alpha)
    return ans



# 参数设置
maxlen = 81
epochs = 5
batch = 32
emb = 300



print('Preprocessing...')
dict = pd.DataFrame(pd.Series(w).value_counts())  # 统计词的出现次数
del w
dict['id'] = list(range(1, len(dict) + 1))
get_sent = lambda x: list(dict['id'][x])
sent = pd.Series(content).apply(get_sent)

for i in range(len(content)):  # 在第一个位置插入view的值，每个句子的第一个词为视角
    a = dict['id'][view[i]]
    sent[i].insert(0,a)

sent = list(sequence.pad_sequences(sent, maxlen=maxlen))


train_content = np.array(sent)
train_opinion = np.array(opinion)
train_opinion1 = np_utils.to_categorical(train_opinion, 3)



print('Build model...')

main_input = Input(shape=(maxlen,), dtype='int32', name='main_input')
x = Embedding(output_dim=emb, input_dim=len(dict)+1, input_length=maxlen, name='x')(main_input)
drop_out = Dropout(0.1, name='dropout')(x)
w_aspect = Lambda(get_aspect, output_shape=(emb,), name="w_aspect")(drop_out)
w_context = Lambda(get_context, output_shape=(maxlen-1,emb), name="w_context")(drop_out)

w_aspect = Dense(emb, W_regularizer=l2(0.01), name="w_aspect_1")(w_aspect)

# hop 1
w_aspects = RepeatVector(maxlen-1, name="w_aspects1")(w_aspect)
merged = merge([w_context, w_aspects], name='merged1', mode='concat')
distributed = TimeDistributed(Dense(1, W_regularizer=l2(0.01), activation='tanh'), name="distributed1")(merged)
flat_alpha = Flatten(name="flat_alpha1")(distributed)
alpha = Dense(maxlen-1, activation='softmax', name="alpha1")(flat_alpha)
w_context_trans = Permute((2, 1), name="w_context_trans1")(w_context)
r_ = merge([w_context_trans, alpha], output_shape=(emb, 1), name="r_1", mode=get_R)
r = Reshape((emb,), name="r1")(r_)
w_aspect_linear = Dense(emb, W_regularizer=l2(0.01), activation='linear')(w_aspect)
merged = merge([r, w_aspect_linear], mode='sum')

w_aspect = Dense(emb, W_regularizer=l2(0.01), name="w_aspect_2")(merged)

# hop 2
w_aspects = RepeatVector(maxlen-1, name="w_aspects2")(w_aspect)
merged = merge([w_context, w_aspects], name='merged2', mode='concat')
distributed = TimeDistributed(Dense(1, W_regularizer=l2(0.01), activation='tanh'), name="distributed2")(merged)
flat_alpha = Flatten(name="flat_alpha2")(distributed)
alpha = Dense(maxlen-1, activation='softmax', name="alpha2")(flat_alpha)
w_context_trans = Permute((2, 1), name="w_context_trans2")(w_context)
r_ = merge([w_context_trans, alpha], output_shape=(emb, 1), name="r_2", mode=get_R)
r = Reshape((emb,), name="r2")(r_)
w_aspect_linear = Dense(emb, W_regularizer=l2(0.01), activation='linear')(w_aspect)
merged = merge([r, w_aspect_linear], mode='sum')

w_aspect = Dense(emb, W_regularizer=l2(0.01), name="w_aspect_3")(merged)

# hop 3
w_aspects = RepeatVector(maxlen-1, name="w_aspects3")(w_aspect)
merged = merge([w_context, w_aspects], name='merged3', mode='concat')
distributed = TimeDistributed(Dense(1, W_regularizer=l2(0.01), activation='tanh'), name="distributed3")(merged)
flat_alpha = Flatten(name="flat_alpha3")(distributed)
alpha = Dense(maxlen-1, activation='softmax', name="alpha3")(flat_alpha)
w_context_trans = Permute((2, 1), name="w_context_trans3")(w_context)
r_ = merge([w_context_trans, alpha], output_shape=(emb, 1), name="r_3", mode=get_R)
r = Reshape((emb,), name="r3")(r_)
w_aspect_linear = Dense(emb, W_regularizer=l2(0.01), activation='linear')(w_aspect)
merged = merge([r, w_aspect_linear], mode='sum')

h_ = Activation('tanh')(merged)
out = Dense(3, activation='softmax')(h_)
output = out
model = Model(input=[main_input], output=output)



model.compile(loss='categorical_crossentropy',
              optimizer=adam(), # 或者SGD(lr=0.03, momentum=0.9, nesterov=True)
              metrics=['accuracy'])



print('Train...')
model.fit(train_content, train_opinion1,
          batch_size=batch, nb_epoch=epochs,
          validation_split=0.1)

实验输出：

注：

通过实验，发现单独使用CNN、 LSTM、fasttext做文本情感分析的效果不如引入Attention的Model效果好，可见如今Attention如此火爆也是有原因的。