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ASIN : B07BWH779J
Publisher : Packt Publishing; 1st edition (June 29, 2018)
Publication date : June 29, 2018
Language : English
File size : 7770 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 308 pages
Natural Language Processing (NLP) and Computational Linguistics are rapidly growing fields that are revolutionizing the way we interact with and analyze text data. In this practical guide, we will explore how to perform various text analysis tasks using popular Python libraries such as Gensim, spaCy, and Keras.
Gensim is a powerful library for topic modeling and document similarity analysis. We will learn how to preprocess text data, train a word embedding model using Word2Vec, and perform topic modeling to extract meaningful topics from a corpus of text.
spaCy is a state-of-the-art NLP library that provides robust tools for text processing, tokenization, named entity recognition, and dependency parsing. We will demonstrate how to use spaCy to perform text classification, sentiment analysis, and entity recognition on a variety of text datasets.
Keras is a high-level neural networks library that is easy to use and allows for fast prototyping of deep learning models. We will showcase how to build and train a text classification model using a recurrent neural network (RNN) with LSTM cells in Keras to classify text data into different categories.
By the end of this guide, you will have a solid understanding of how to perform text analysis tasks using Python and popular NLP libraries. Whether you are a beginner or an experienced data scientist, this guide will provide you with the necessary tools and techniques to effectively analyze and extract valuable insights from text data. Get ready to dive into the world of NLP and Computational Linguistics with this practical guide!
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