情感分析的基本方法有：詞法分析，基於機器學習的分析，混合分析

詞法分析運用了由預標記詞彙組成的詞典，使用詞法分析器將輸入文字轉換為單詞序列，將每個新的單詞與字典中的詞彙進行匹配。

機器學習方法的關鍵是合適特徵的選擇。通常有unigram,bigrams,trigrams選為特徵向量

實戰電影評論情感分析

分為5部分

1、訓練或者載入一個詞向量生成模型

2、建立一個用於訓練集的ID矩陣

3、建立LSTM計算單元

4、訓練

5、測試

step1： 載入並分析資料

# encoding:utf-8

import numpy as np

wordsList = np.load('wordsList.npy')
print('載入word列表')
wordsList = wordsList.tolist()
wordsList = [word.decode('UTF-8')
             for word in wordsList]
wordVectors = np.load('wordVectors.npy')
print('載入文字向量')

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('正面評價完結')

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('負面評價完結')

num_files = len(num_words)
print('檔案總數', num_files)
print('所有的詞的數量', sum(num_words))
print('平均檔案詞的長度', sum(num_words) / len(num_words))
 

step2：將文字生成一個索引矩陣

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  # 未知的詞
      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  # 未知的詞語
      indexCounter = indexCounter + 1
      if indexCounter >= max_seq_num:
        break
    file_count = file_count + 1

np.save('idsMatrix', ids)
 

step3： 輔助函式，用來生成一批訓練資料集

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：模型設定

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)
獲得文字向量
data = tf.nn.embedding_lookup(wordVectors, input_data)

配置LSTM的數量
lstmCell = tf.contrib.rnn.BasicLSTMCell(lstm_units)
避免過擬合
lstmCell = tf.contrib.rnn.DropoutWrapper(cell=lstmCell, output_keep_prob=0.5)
輸入模型中，用來展開整個網路
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)

定義正確的評估函式以及正確率評估引數
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, " 正確率:", (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)