You're reading from Hands-On Natural Language Processing with PyTorch 1.x

Product typeBook

Published inJul 2020

Reading LevelBeginner

PublisherPackt

ISBN-139781789802740

Edition1st Edition

Languages

Python

Tools

PyTorch

Concepts

Mobile Application Development

Author (1)

Thomas Dop

Building a sentiment analyzer using LSTMs

We will now look at how to build our own simple LSTM to categorize sentences based on their sentiment. We will train our model on a dataset of 3,000 reviews that have been categorized as positive or negative. These reviews come from three different sources—film reviews, product reviews, and location reviews—in order to ensure that our sentiment analyzer is robust. The dataset is balanced so that it consists of 1,500 positive reviews and 1,500 negative reviews. We will start by importing our dataset and examining it:

with open("sentiment labelled sentences/sentiment.txt") as f:
    reviews = f.read()
    
data = pd.DataFrame([review.split('\t') for review in                      reviews.split('\n')])
data.columns = ['Review','Sentiment']...

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Hands-On Natural Language Processing with PyTorch 1.x

Published in: Jul 2020Publisher: PacktISBN-13: 9781789802740

Author (1)

Thomas Dop

Thomas Dop is a data scientist at MagicLab, a company that creates leading dating apps, including Bumble and Badoo. He works on a variety of areas within data science, including NLP, deep learning, computer vision, and predictive modeling. He holds an MSc in data science from the University of Amsterdam.
Read more about Thomas Dop

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You're reading from Hands-On Natural Language Processing with PyTorch 1.x

Building a sentiment analyzer using LSTMs

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Getting Started with Google BERT will help you become well-versed with the BERT model from scratch and learn how to create interesting NLP applications. You'll understand several variants of BERT such as ALBERT, RoBERTa, DistilBERT, ELECTRA, VideoBERT, and many others in detail.

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