python_NLP实战之情感分析_nlp情感分析模型训练 python

情感分析的基本方法有：词法分析，基于机器学习的分析，混合分析

词法分析运用了由预标记词汇组成的词典，使用词法分析器将输入文本转换为单词序列，将每个新的单词与字典中的词汇进行匹配。

机器学习方法的关键是合适特征的选择。通常有unigram,bigrams,trigrams选为特征向量

实战电影评论情感分析

分为5部分

1、训练或者载入一个词向量生成模型

2、创建一个用于训练集的ID矩阵

3、创建LSTM计算单元

4、训练

5、测试

step1： 载入并分析数据

# encoding:utf-8
 
import numpy as np
 
wordsList = np.load('wordsList.npy')
print('载入word列表')
wordsList = wordsList.tolist()
wordsList = [word.decode('UTF-8')
             for word in wordsList]
wordVectors = np.load('wordVectors.npy')
print('载入文本向量')
 
print(len(wordsList))
print(wordVectors.shape)
 
import os
from os.path import isfile, join
 
pos_files = ['pos/' + f for f in os.listdir(
    'pos/') if isfile(join('pos/', f))]
neg_files = ['neg/' + f for f in os.listdir(
    'neg/') if isfile(join('neg/', f))]
num_words = []
for pf in pos_files:
    with open(pf, "r", encoding='utf-8') as f:
        line = f.readline()
        counter = len(line.split())
        num_words.append(counter)
print('正面评价完结')
 
for nf in neg_files:
    with open(nf, "r", encoding='utf-8') as f:
        line = f.readline()
        counter = len(line.split())
        num_words.append(counter)
print('负面评价完结')
 
num_files = len(num_words)
print('文件总数', num_files)
print('所有的词的数量', sum(num_words))
print('平均文件词的长度', sum(num_words) / len(num_words))

step2：将文本生成一个索引矩阵

import re
 
strip_special_chars = re.compile("[^A-Za-z0-9 ]+")
num_dimensions = 300  # Dimensions for each word vector
 
 
def cleanSentences(string):
    string = string.lower().replace("<br />", " ")
    return re.sub(strip_special_chars, "", string.lower())
 
 
max_seq_num = 250
ids = np.zeros((num_files, max_seq_num), dtype='int32')
file_count = 0
for pf in pos_files:
  with open(pf, "r", encoding='utf-8') as f:
    indexCounter = 0
    line = f.readline()
    cleanedLine = cleanSentences(line)
    split = cleanedLine.split()
    for word in split:
      try:
        ids[file_count][indexCounter] = wordsList.index(word)
      except ValueError:
        ids[file_count][indexCounter] = 399999  # 未知的词
      indexCounter = indexCounter + 1
      if indexCounter >= max_seq_num:
        break
    file_count = file_count + 1
 
for nf in neg_files:
  with open(nf, "r",encoding='utf-8') as f:
    indexCounter = 0
    line = f.readline()
    cleanedLine = cleanSentences(line)
    split = cleanedLine.split()
    for word in split:
      try:
        ids[file_count][indexCounter] = wordsList.index(word)
      except ValueError:
        ids[file_count][indexCounter] = 399999  # 未知的词语
      indexCounter = indexCounter + 1
      if indexCounter >= max_seq_num:
        break
    file_count = file_count + 1
 
np.save('idsMatrix', ids)

step3： 辅助函数，用来生成一批训练数据集

def get_train_batch():
    labels = []
    arr = np.zeros([batch_size, max_seq_num])
    for i in range(batch_size):
        if (i % 2 == 0):
            num = randint(1, 11499)
            labels.append([1, 0])
        else:
            num = randint(13499, 24999)
            labels.append([0, 1])
        arr[i] = ids[num - 1:num]
    return arr, labels
 
 
def get_test_batch():
    labels = []
    arr = np.zeros([batch_size, max_seq_num])
    for i in range(batch_size):
        num = randint(11499, 13499)
        if (num <= 12499):
            labels.append([1, 0])
        else:
            labels.append([0, 1])
        arr[i] = ids[num - 1:num]
    return arr, labels

step4：模型设置

import tensorflow as tf
 
tf.reset_default_graph()
 
labels = tf.placeholder(tf.float32, [batch_size, num_labels])
input_data = tf.placeholder(tf.int32, [batch_size, max_seq_num])
data = tf.Variable(
    tf.zeros([batch_size, max_seq_num, num_dimensions]), dtype=tf.float32)
获得文本向量
data = tf.nn.embedding_lookup(wordVectors, input_data)
 
配置LSTM的数量
lstmCell = tf.contrib.rnn.BasicLSTMCell(lstm_units)
避免过拟合
lstmCell = tf.contrib.rnn.DropoutWrapper(cell=lstmCell, output_keep_prob=0.5)
输入模型中，用来展开整个网络
value, _ = tf.nn.dynamic_rnn(lstmCell, data, dtype=tf.float32)
 
weight = tf.Variable(tf.truncated_normal([lstm_units, num_labels]))
bias = tf.Variable(tf.constant(0.1, shape=[num_labels]))
value = tf.transpose(value, [1, 0, 2])
last = tf.gather(value, int(value.get_shape()[0]) - 1)
prediction = (tf.matmul(last, weight) + bias)
 
定义正确的评估函数以及正确率评估参数
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=prediction, labels=labels))
optimizer = tf.train.AdamOptimizer(lr).minimize(loss)
 
saver = tf.train.Saver()
 
with tf.Session() as sess:
    if os.path.exists("models") and os.path.exists("models/checkpoint"):
        saver.restore(sess, tf.train.latest_checkpoint('models'))
    else:
        if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
            init = tf.initialize_all_variables()
        else:
            init = tf.global_variables_initializer()
        sess.run(init)
 
    iterations = 100
    for step in range(iterations):
        next_batch, next_batch_labels = get_test_batch()
        if step % 20 == 0:
            print("step:", step, " 正确率:", (sess.run(
                accuracy, {input_data: next_batch, labels: next_batch_labels})) * 100)
 
    if not os.path.exists("models"):
        os.mkdir("models")
    save_path = saver.save(sess, "models/model.ckpt")
    print("Model saved in path: %s" % save_path)