标签：效果 dict rom 过程 upd 类别 模型 向量 0.00
使用卷积神经网络对汽车行业评论文本进行情感分析。
爬取汽车之家车主口碑评论文本，抽取口碑中最满意以及最不满意评论文本，分别作为正向情感语料库和负向情感语料库。
语料库基本信息如下：
训练集(data/ch_auto_train.txt): 40000 = 20000(pos) + 20000(neg) 验证集(data/ch_auto_dev.txt): 10000 = 5000(pos) + 5000(neg) 测试集(data/ch_auto_test.txt): 20000 = 10000(pos) + 10000(neg)
utils.py为数据的预处理代码。
cat_to_id(): 分类类别以及id对应词典{pos:0, neg:1};
build_word2id(): 构建词汇表并存储，形如{word: id};
load_word2id(): 加载上述构建的词汇表;
build_word2vec(): 基于预训练好的word2vec构建训练语料中所含词语的word2vec;
load_corpus_word2vec(): 加载上述构建的word2ve;
load_corpus(): 加载语料库：train/dev/test;
batch_index(): 生成批处理id序列。
经过数据预处理，数据的格式如下：
x: [1434, 5454, 2323, ..., 0, 0, 0] y: [0, 1]
x为构成一条语句的单词所对应的id。 y为onehot编码: pos-[1, 0], neg-[0, 1]。
CNN可配置的参数如下所示，在cnn_model.py中。
class CONFIG(): update_w2v = True # 是否在训练中更新w2v vocab_size = 37814 # 词汇量，与word2id中的词汇量一致 n_class = 2 # 分类数：分别为pos和neg max_sen_len = 75 # 句子最大长度 embedding_dim = 50 # 词向量维度 batch_size = 100 # 批处理尺寸 n_hidden = 256 # 隐藏层节点数 n_epoch = 10 # 训练迭代周期，即遍历整个训练样本的次数 opt = ‘adam‘ # 训练优化器：adam或者adadelta learning_rate = 0.001 # 学习率；若opt=‘adadelta‘，则不需要定义学习率 drop_keep_prob = 0.5 # dropout层，参数keep的比例 num_filters = 256 # 卷积层filter的数量 kernel_size = 3 # 卷积核的尺寸；nlp任务中通常选择2,3,4,5 print_per_batch = 100 # 训练过程中,每100词batch迭代，打印训练信息 save_dir = ‘./checkpoints/‘ # 训练模型保存的地址 ...
train_and_eva.py中的train()进行训练。
加载word2vec========================== 加载train语料库======================== 总样本数为：40000 各个类别样本数如下： pos 20000 neg 20000 加载dev语料库========================== 总样本数为：10000 各个类别样本数如下： pos 5000 neg 5000 加载test语料库========================= 总样本数为：20000 各个类别样本数如下： pos 10000 neg 10000 Training and evaluating... Epoch: 1 Iter: 0, Train Loss: 0.71, Train Acc: 51.00%, Val Loss: 0.86, Val Acc: 49.96%, Time: 0:00:04 * Iter: 100, Train Loss: 0.29, Train Acc: 89.00%, Val Loss: 0.26, Val Acc: 89.16%, Time: 0:04:37 * Iter: 200, Train Loss: 0.22, Train Acc: 93.00%, Val Loss: 0.2, Val Acc: 91.85%, Time: 0:09:05 * Iter: 300, Train Loss: 0.082, Train Acc: 96.00%, Val Loss: 0.17, Val Acc: 93.26%, Time: 0:13:26 * Epoch: 2 Iter: 400, Train Loss: 0.16, Train Acc: 96.00%, Val Loss: 0.17, Val Acc: 93.19%, Time: 0:17:52 Iter: 500, Train Loss: 0.11, Train Acc: 97.00%, Val Loss: 0.17, Val Acc: 93.51%, Time: 0:22:11 * Iter: 600, Train Loss: 0.16, Train Acc: 97.00%, Val Loss: 0.15, Val Acc: 94.22%, Time: 0:26:36 * Iter: 700, Train Loss: 0.15, Train Acc: 91.00%, Val Loss: 0.15, Val Acc: 94.05%, Time: 0:30:54 Epoch: 3 Iter: 800, Train Loss: 0.11, Train Acc: 95.00%, Val Loss: 0.15, Val Acc: 94.13%, Time: 0:35:13 Iter: 900, Train Loss: 0.058, Train Acc: 97.00%, Val Loss: 0.16, Val Acc: 94.33%, Time: 0:39:37 * Iter: 1000, Train Loss: 0.048, Train Acc: 98.00%, Val Loss: 0.15, Val Acc: 94.33%, Time: 0:43:53 Iter: 1100, Train Loss: 0.054, Train Acc: 97.00%, Val Loss: 0.16, Val Acc: 94.10%, Time: 0:48:21 Epoch: 4 Iter: 1200, Train Loss: 0.065, Train Acc: 96.00%, Val Loss: 0.16, Val Acc: 94.52%, Time: 0:52:43 * Iter: 1300, Train Loss: 0.056, Train Acc: 97.00%, Val Loss: 0.17, Val Acc: 94.55%, Time: 0:57:09 * Iter: 1400, Train Loss: 0.016, Train Acc: 100.00%, Val Loss: 0.17, Val Acc: 94.40%, Time: 1:01:30 Iter: 1500, Train Loss: 0.1, Train Acc: 97.00%, Val Loss: 0.16, Val Acc: 94.90%, Time: 1:05:49 * Epoch: 5 Iter: 1600, Train Loss: 0.021, Train Acc: 99.00%, Val Loss: 0.16, Val Acc: 94.28%, Time: 1:10:00 Iter: 1700, Train Loss: 0.045, Train Acc: 99.00%, Val Loss: 0.18, Val Acc: 94.40%, Time: 1:14:16 Iter: 1800, Train Loss: 0.036, Train Acc: 98.00%, Val Loss: 0.21, Val Acc: 94.10%, Time: 1:18:36 Iter: 1900, Train Loss: 0.014, Train Acc: 100.00%, Val Loss: 0.2, Val Acc: 94.18%, Time: 1:22:59
在验证集上的最佳效果为94.90%。
train_and_eva.py中的test()进行测试。
INFO:tensorflow:Restoring parameters from ./checkpoints/sa-model Precision, Recall and F1-Score... precision recall f1-score support pos 0.96 0.96 0.96 10000 neg 0.96 0.96 0.96 10000 avg / total 0.96 0.96 0.96 20000 Confusion Matrix... [[9597 403] [ 449 9551]]
在测试集上的准确率达到了95.74%，且各类的precision, recall和f1-score都超过了95%。
predict.py中的predict()进行预测
>> test = [‘噪音大、车漆很薄‘, ‘性价比很高，价位不高，又皮实耐用。‘] >> print(predict(test, label=True)) INFO:tensorflow:Restoring parameters from ./checkpoints/sa-model [‘neg‘, ‘pos‘]
标签：效果 dict rom 过程 upd 类别 模型 向量 0.00
原文地址：https://www.cnblogs.com/darwinli/p/10011378.html
