Mastering Long Short-Term Memory with Python: Unleashing the Power of LSTM in NLP

A comprehensive guide to understanding and implementing LSTM layers for natural language processing with Python

Photo by Sven Brandsma on Unsplash

This work is a continuation of my article about RNNs and NLP with Python. A natural progression of a deep learning network with a simple recurrent layer is a deep learning network with a Long Short Term Memory (LSTM for short) layer.

As with the RNN and NLP, I will try to explain the LSTM layer in great detail and code the forward pass of the layer from scratch.

All the codes can be viewed here: https://github.com/Eligijus112/NLP-python

We will work with the same dataset¹ as in the previous article:

# Data wrangling
import pandas as pd

# Reading the data 
d = pd.read_csv('input/Tweets.csv', header=None)

# Adding the columns 
d.columns = ['INDEX', 'GAME', "SENTIMENT", 'TEXT']

# Leaving only the positive and the negative sentiments 
d = d[d['SENTIMENT'].isin(['Positive', 'Negative'])]

# Encoding the sentiments that the negative will be 1 and the positive 0
d['SENTIMENT'] = d['SENTIMENT'].apply(lambda x: 0 if x == 'Positive' else 1)

# Dropping missing values
d = d.dropna()