[Tensorflow] RNN - 02. Movie Review Sentiment Prediction with LSTM

Predicting Movie Review Sentiment with TensorFlow and TensorBoard

Ref: http://www.cnblogs.com/libinggen/p/6939577.html

Ref: https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

使用LSTM的原因之一是: 解决RNN Deep Network的Gradient错误累积太多，以至于Gradient归零或者成为无穷大，所以无法继续进行优化的问题。

Thanks to Jürgen Schmidhuber

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Using the data from an old Kaggle competition “Bag of Words Meets Bags of Popcorn” 

import pandas as pd
import numpy as np
import tensorflow as tf
import nltk, re, time
from nltk.corpus import stopwords
from collections import defaultdict
from tqdm import tqdm
from sklearn.model_selection import train_test_split
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from collections import namedtuple

 

Preprocessing 

The data is formatted as .tsv

• remove stopwords
• Convert words to lower case

def clean_text(text, remove_stopwords=True):
    '''Clean the text, with the option to remove stopwords'''
    
    # Convert words to lower case and split them
    text = text.lower().split()

    # Optionally, remove stop words
    if remove_stopwords:
        stops = set(stopwords.words("english"))
        text = [w for w in text if not w in stops]
    
    text = " ".join(text)

    # Clean the text
    text = re.sub(r"<br />", " ", text)
    text = re.sub(r"[^a-z]", " ", text)
    text = re.sub(r"   ", " ", text) # Remove any extra spaces
    text = re.sub(r"  ", " ", text)
    
    # Return a list of words
    return(text)

Data clean