The Ultimate Practical Guide To Text Analysis With Python Gensim Spacy And Keras! Unlock the Secrets of Natural Language Processing and Enhance your Text Analysis Skills Today!

Text analysis has become an essential skill in the digital age. The ability to extract valuable insights from large volumes of text can provide a competitive edge to businesses, drive decision-making processes, and improve user experiences. Python, an incredibly versatile programming language, offers powerful tools and libraries to perform text analysis effectively.

In this comprehensive guide, we will explore three popular Python libraries for text analysis: Gensim, Spacy, and Keras. With the knowledge gained from this guide, you will be able to leverage the full potential of these libraries to build robust text analysis solutions.

Chapter 1: Gensim

Gensim is a Python library designed specifically for unsupervised topic modeling and document similarity analysis. With Gensim, you can easily classify documents based on topic modeling, extract keywords from text, and find similar documents within a corpus. We will delve into Gensim's core concepts, such as corpora, vector spaces, and topic modeling algorithms, and explain step-by-step how to apply them to real-life scenarios.

Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras

by Andrew Luria (1st Edition, Kindle Edition)

4.1 out of 5

Language	:	English
File size	:	8169 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	308 pages
Screen Reader	:	Supported

FREE

Chapter 2: Spacy

Spacy is a leading Python library for natural language processing (NLP). It provides a wide range of functionalities, including tokenization, named entity recognition, part-of-speech tagging, dependency parsing, and more. We will explore how to leverage Spacy's powerful NLP capabilities to preprocess text data, extract meaningful information, and perform advanced text analysis tasks.

Chapter 3: Keras

Keras is a high-level neural networks library built on top of TensorFlow. With Keras, you can easily build and train deep learning models for text classification, sentiment analysis, and other applications. We will guide you through the process of constructing a text classification model using Keras, including data preprocessing, model building, and evaluation. Additionally, we will cover advanced techniques such as word embeddings and recurrent neural networks for more sophisticated text analysis tasks.

Chapter 4: Practical Examples and Case Studies

In this final chapter, we will provide practical examples and case studies to showcase the power of text analysis with Gensim, Spacy, and Keras. You will see how these libraries can be utilized to solve real-world problems, such as sentiment analysis of customer reviews, document clustering, and content recommendation.

By the end of this guide, you will have a solid understanding of text analysis techniques and be equipped with the skills to apply them in your own projects. Whether you are a data scientist, business analyst, or a curious learner, this guide will enable you to unlock the secrets of natural language processing and enhance your text analysis skills with Python!

Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras

by Andrew Luria (1st Edition, Kindle Edition)

4.1 out of 5

Language	:	English
File size	:	8169 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	308 pages
Screen Reader	:	Supported

FREE

Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms.

Key Features

• Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras
• Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms
• Learn deep learning techniques for text analysis

Book Description

Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.

This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.

You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.

This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.

What you will learn

• Why text analysis is important in our modern age
• Understand NLP terminology and get to know the Python tools and datasets
• Learn how to pre-process and clean textual data
• Convert textual data into vector space representations
• Using spaCy to process text
• Train your own NLP models for computational linguistics
• Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn
• Employ deep learning techniques for text analysis using Keras

Who This Book Is For

This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!

Table of Contents

1. What is Text Analysis?
2. Python Tips for Text Analysis
3. spaCy’s Language Models
4. Gensim – Vectorizing text and transformations and n-grams
5. POS-Tagging and its Applications
6. NER-Tagging and its Applications
7. Dependency Parsing
8. Top Models
9. Advanced Topic Modelling
10. Clustering and Classifying Text
11. Similarity Queries and Summarization
12. Word2Vec, Doc2Vec and Gensim
13. Deep Learning for text
14. Keras and spaCy for Deep Learning
15. Sentiment Analysis and ChatBots