本文使用哈工大做文本预处理； 两层隐层神经网络； 后注：不是标准的ann，做了去停用词和词性筛选，没有端到端。
# -*- coding: utf-8 -*- # @bref :使用tensorflow做中文情感分析 import numpy as np import tensorflow as tf import random from sklearn.feature_extraction.text import CountVectorizer import os import traceback real_dir_path = os.path.split(os.path.realpath(__file__))[0] pos_file = os.path.join(real_dir_path, 'data/pos_bak.txt') neg_file = os.path.join(real_dir_path, 'data/neg_bak.txt') #使用哈工大分词和词性标注 from pyltp import Segmentor, Postagger seg = Segmentor() seg.load('/root/git/ltp_data/cws.model') poser = Postagger() poser.load('/root/git/ltp_data/pos.model') real_dir_path = os.path.split(os.path.realpath(__file__))[0] #文件所在路径 stop_words_file = os.path.join(real_dir_path, '../util/stopwords.txt') #定义允许的词性 allow_pos_ltp = ('a', 'i', 'j', 'n', 'nh', 'ni', 'nl', 'ns', 'nt', 'nz', 'v', 'ws') #分词、去除停用词、词性筛选 def cut_stopword_pos(s): words = seg.segment(''.join(s.split())) poses = poser.postag(words) stopwords = {}.fromkeys([line.rstrip() for line in open(stop_words_file)]) sentence = [] for i, pos in enumerate(poses): if (pos in allow_pos_ltp) and (words[i] not in stopwords): sentence.append(words[i]) return sentence def create_vocab(pos_file, neg_file): def process_file(file_path): with open(file_path, 'r') as f: v = [] lines = f.readlines() for line in lines: sentence = cut_stopword_pos(line) v.append(' '.join(sentence)) return v sent = process_file(pos_file) sent += process_file(neg_file) tf_v = CountVectorizer(max_df=0.9, min_df=1) tf = tf_v.fit_transform(sent) #print tf_v.vocabulary_ return tf_v.vocabulary_.keys() #获取词汇 vocab = create_vocab(pos_file, neg_file) #依据词汇将评论转化为向量 def normalize_dataset(vocab): dataset = [] # vocab:词汇表; review:评论; clf:评论对应的分类, [0, 1]表示负面评论,[1, 0]表示正面 def string_to_vector(vocab, review, clf): words = cut_stopword_pos(review) # list of str features = np.zeros(len(vocab)) for w in words: if w.decode('utf-8') in vocab: features[vocab.index(w.decode('utf-8'))] = 1 return [features, clf] with open(pos_file, 'r') as f: lines = f.readlines() for line in lines: one_sample = string_to_vector(vocab, line, [1, 0]) dataset.append(one_sample) with open(neg_file, 'r') as f: lines = f.readlines() for line in lines: one_sample = string_to_vector(vocab, line, [0, 1]) dataset.append(one_sample) return dataset dataset = normalize_dataset(vocab) random.shuffle(dataset) #打乱顺序 #取样本的10%作为测试数据 test_size = int(len(dataset) * 0.1) dataset = np.array(dataset) train_dataset = dataset[:-test_size] test_dataset = dataset[-test_size:] print 'test_size = {}'.format(test_size) #print 'size of train_dataset is {}'.format(train_dataset) #Feed-forward nueral network #定义每个层有多少个神经元 n_input_layer = len(vocab) #输入层每个神经元代表一个term n_layer_1 = 1000 #hiden layer n_layer_2 = 1000 # hiden layer n_output_layer = 2 #定义待训练的神经网络 def neural_netword(data): #定义第一层神经元的w和b, random_normal定义服从正态分布的随机变量 layer_1_w_b = {'w_':tf.Variable(tf.random_normal([n_input_layer, n_layer_1])), 'b_':tf.Variable(tf.random_normal([n_layer_1]))} layer_2_w_b = {'w_':tf.Variable(tf.random_normal([n_layer_1, n_layer_2])), 'b_':tf.Variable(tf.random_normal([n_layer_2]))} layer_output_w_b = {'w_':tf.Variable(tf.random_normal([n_layer_2, n_output_layer])), 'b_':tf.Variable(tf.random_normal([n_output_layer]))} layer_1 = tf.add(tf.matmul(data, layer_1_w_b['w_']), layer_1_w_b['b_']) layer_1 = tf.nn.relu(layer_1) #relu做激活函数 layer_2 = tf.add(tf.matmul(layer_1, layer_2_w_b['w_']), layer_2_w_b['b_']) layer_2 = tf.nn.relu(layer_2) layer_output = tf.add(tf.matmul(layer_2, layer_output_w_b['w_']), layer_output_w_b['b_']) return layer_output batch_size = 50 X = tf.placeholder('float', [None, n_input_layer]) #None表示样本数量任意; 每个样本纬度是term数量 Y = tf.placeholder('float') #使用数据训练神经网络 def train_neural_network(X, Y): predict = neural_netword(X) #cost func是输出层softmax的cross entropy的平均值。 将softmax 放在此处而非nn中是为了效率. cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=Y)) #设置优化器 optimizer = tf.train.AdamOptimizer().minimize(cost_func) epochs = 13 #epoch本意是时代、纪, 这里是迭代周期 with tf.Session() as session: session.run(tf.initialize_all_variables()) #初始化所有变量,包括w,b random.shuffle(train_dataset) train_x = train_dataset[:, 0] #每一行的features; train_y = train_dataset[:, 1] #每一行的label print 'size of train_x is {}'.format(len(train_x)) for epoch in range(epochs): epoch_loss = 0 #每个周期的loss i = 0 while i < len(train_x): start = i end = i + batch_size batch_x = train_x[start:end] batch_y = train_y[start:end] #run的第一个参数fetches可以是单个,也可以是多个。 返回值是fetches的返回值。 #此处因为要打印cost,所以cost_func也在fetches中 _, c = session.run([optimizer, cost_func], feed_dict={X:list(batch_x), Y:list(batch_y)}) epoch_loss += c i = end print(epoch, ' : ', epoch_loss) #评估模型 test_x = test_dataset[:, 0] test_y = test_dataset[:, 1] #argmax能给出某个tensor对象在某一维上的其数据最大值所在的索引值, 这里是索引值的list。tf.equal用于检测匹配,返回bool型的list correct = tf.equal(tf.argmax(predict, 1), tf.argmax(Y, 1)) #tf.cast 可以将[True, False, True] 转化为[1, 0, 1] #reduce_mean用于在某一维上计算平均值, 未指定纬度则计算所有元素 accurqcy = tf.reduce_mean(tf.cast(correct, 'float')) print('准确率: {}'.format(accurqcy.eval({X:list(test_x), Y:list(test_y)}))) #等价: print session.run(accuracy, feed_dict={X:list(test_x), Y:list(test_y)}) train_neural_network(X, Y)最终的执行显示：
size of train_x is 31612 (0, ' : ', 105508.38228607178) (1, ' : ', 11773.463727131188) (2, ' : ', 4551.4978754326503) (3, ' : ', 3576.6907950473492) (4, ' : ', 3144.6771814899175) (5, ' : ', 2911.1803286887775) (6, ' : ', 2691.8284285693276) (7, ' : ', 2651.9982114042473) (8, ' : ', 2882.4479921576026) (9, ' : ', 2665.3818837262743) (10, ' : ', 2551.3030235993206) (11, ' : ', 2838.3546982686303) (12, ' : ', 2770.5539811982608) 准确率: 0.828587830067