机器学习|基于LSTM的IMDB电影评论情感分析 lstm|自然语言处理|python|python

基于LSTM的IMDB电影评论情感分析步骤

加载数据 (50K IMDB Movie Review)
数据清洗
编码“情感”
数据集划分（训练集和测试集）
对评论进行分词和截断/补零操作
构建神经网络模型
训练模型并测试

导入相关工具箱

import pandas as pd# to load dataset import numpy as np# for mathematic equation import nltk from nltk.corpus import stopwords# to get collection of stopwords from sklearn.model_selection import train_test_split# for splitting dataset from tensorflow.keras.preprocessing.text import Tokenizer# to encode text to int from tensorflow.keras.preprocessing.sequence import pad_sequences# to do padding or truncating from tensorflow.keras.models import Sequential# the model from tensorflow.keras.layers import Embedding, LSTM, Dense,Dropout # layers of the architecture from tensorflow.keras.optimizers import Adam from tensorflow.keras.callbacks import ModelCheckpoint# save model from tensorflow.keras.models import load_model# load saved model import re

预览数据集

data = https://www.it610.com/article/pd.read_csv('IMDB Dataset.csv')print(data)

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Stop Word is a commonly used words in a sentence, usually a search engine is programmed to ignore this words (i.e. "the", "a", "an", "of", etc.) 设置停用词，英文中常见的停用词
此处需要下载nltk的stopwords，我选择了百度网盘，直接下载然后放到一个文件夹中D:\nltk_data\corpora

#nltk.download('stopwords') english_stops = set(stopwords.words('english'))

数据清洗
原始评论数据并不“干净”，存在很多html标签、数字、大写字母、标点符号等等。预处理中需要对评论做预处理，包括去掉html标签，过滤掉停用词，删除标点和数字，将大写字母转成小写
编码正负情感
将正面和负面情感编码成整数，这里0代表负面情感negative，1代表正面情感（positive）

def load_dataset(): df = pd.read_csv('IMDB Dataset.csv') x_data = https://www.it610.com/article/df['review']# Reviews/Input y_data = https://www.it610.com/article/df['sentiment']# Sentiment/Output# PRE-PROCESS REVIEW x_data = https://www.it610.com/article/x_data.replace({'<.*?>': ''}, regex = True)# remove html tag x_data = https://www.it610.com/article/x_data.replace({'[^A-Za-z]': ' '}, regex = True)# remove non alphabet x_data = https://www.it610.com/article/x_data.apply(lambda review: [w for w in review.split() if w not in english_stops])# remove stop words x_data = x_data.apply(lambda review: [w.lower() for w in review])# lower case# ENCODE SENTIMENT -> 0 & 1 y_data = https://www.it610.com/article/y_data.replace('positive', 1) y_data = https://www.it610.com/article/y_data.replace('negative', 0)return x_data, y_datax_data, y_data = https://www.it610.com/article/load_dataset()print('Reviews') print(x_data, '\n') print('Sentiment') print(y_data)

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划分数据集
80% 作为训练集
20% 作为测试集
，使用Scikit-Learn中的 train_test_split 方法。

x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size = 0.2)print('Train Set') print(x_train, '\n') print(x_test, '\n') print('Test Set') print(y_train, '\n') print(y_test)

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该函数能够输出训练集中单条评论最多的词语个数

def get_max_length(): review_length = [] for review in x_train: review_length.append(len(review))return int(np.ceil(np.mean(review_length)))

分词以及截断和补零

# ENCODE REVIEW token = Tokenizer(lower=False)# no need lower, because already lowered the data in load_data() token.fit_on_texts(x_train) x_train = token.texts_to_sequences(x_train) x_test = token.texts_to_sequences(x_test)max_length = get_max_length() print(max_length)x_train = pad_sequences(x_train, maxlen=max_length, padding='post', truncating='post') x_test = pad_sequences(x_test, maxlen=max_length, padding='post', truncating='post')total_words = len(token.word_index) + 1# add 1 because of 0 paddingprint('Encoded X Train\n', x_train, '\n') print('Encoded X Test\n', x_test, '\n') print('Maximum review length: ', max_length)

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构建深度神经网络模型
Embedding Layer
LSTM Layer
Dense Layer
怎么调参：
EMBED_DIM
LSTM_OUT：LSTM输出维度，可以修改
深度可以改变：增减LSTM层，但是数据较少，过于多的层数会出现过拟合的现象，对于该小实验，不用过多的层数
input_length:输入单词的长度，原本为130，可以设的更小一点
epochs可以改为10，到10次迭代的时候可能收敛。
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128, return_sequences=True))：可以修改为双向的LSTM

# ARCHITECTURE EMBED_DIM =16 LSTM_OUT = 8model = Sequential() model.add(Embedding(total_words, EMBED_DIM, input_length = max_length)) model.add(LSTM(LSTM_OUT, return_sequences=True)) model.add(LSTM(LSTM_OUT, return_sequences=True)) model.add(LSTM(LSTM_OUT, return_sequences=True)) model.add(LSTM(LSTM_OUT, return_sequences=True)) model.add(LSTM(LSTM_OUT)) model.add(Dropout(0.2)) model.add(Dense(1, activation='sigmoid')) model.compile(optimizer = Adam(1e-3), loss = 'binary_crossentropy', metrics = ['accuracy'])#metrics分类精度print(model.summary())

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训练模型
【机器学习|基于LSTM的IMDB电影评论情感分析】batch_size 设置为128 ,epochs设置为5
添加 checkpoint当每个epoch训练完成精度有提升时，就更新保存的模型
epoches5轮训练
batch_size每次送入128个进入模型训练，cpu好就可以设置大一些
checkpoint每轮跑完训练好的模型进行保存，save_best_only=true最后只保存精度最高的模型
文件下有一个目录，模型自动存储在models/LSTM.h5，下次可以不用训练了，每次直接调用模型就可以了。

checkpoint = ModelCheckpoint( 'models/LSTM.h6', monitor='accuracy', save_best_only=True, verbose=1 )model.fit(x_train, y_train, batch_size = 128, epochs = 5, callbacks=[checkpoint])

y_pred = model.predict(x_test, batch_size = 64) true = 0 for i, y in enumerate(y_test): if y_pred[i]>=0.7: y_pred[i]=1 else: y_pred[i]=0 if y == y_pred[i]: true += 1print('Correct Prediction: {}'.format(true)) print('Wrong Prediction: {}'.format(len(y_pred) - true)) print('Accuracy: {}'.format(true/len(y_pred)*100))

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测试模型
测试样本测试，输出精度

y_pred = model.predict(x_test, batch_size = 64) y_pred

y_pred = model.predict(x_test, batch_size = 128) y_pred=np.argmax(y_pred,axis=-1) true = 0 for i, y in enumerate(y_test): if y == y_pred[i]: true += 1print('Correct Prediction: {}'.format(true)) print('Wrong Prediction: {}'.format(len(y_pred) - true)) print('Accuracy: {}'.format(true/len(y_pred)*100))

加载已训练好的模型
用已经训练好的深度模型去预测一条新的电影评论的情感

loaded_model = load_model('models/LSTM.h5')

自行输入一条评论（用英文）

review = str(input('Movie Review: '))

预测之前这条新的评论也需要进行预处理

# Pre-process input regex = re.compile(r'[^a-zA-Z\s]') review = regex.sub('', review) print('Cleaned: ', review)words = review.split(' ') filtered = [w for w in words if w not in english_stops] filtered = ' '.join(filtered) filtered = [filtered.lower()]print('Filtered: ', filtered)

对这条新评论进行分词和编码

tokenize_words = token.texts_to_sequences(filtered) tokenize_words = pad_sequences(tokenize_words, maxlen=max_length, padding='post', truncating='post') print(tokenize_words)

结果显示了预测的置信度分数 confidence score