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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook - Second Edition

By : Zhenya Antić, Saurabh Chakravarty
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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

5 (5)
By: Zhenya Antić, Saurabh Chakravarty

Overview of this book

Harness the power of Natural Language Processing (NLP) to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess. You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs. This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust in your NLP models. By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.
Table of Contents (13 chapters)
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Visualizing topics from BERTopic

In this recipe, we will create and visualize a BERTopic model on the BBC data. There are several visualizations available with the BERTopic package, and we will use several of them.

In this recipe, we will create a topic model in a similar fashion as in Chapter 6, in the Topic modeling using BERTopic recipe. However, unlike in Chapter 6, we will not limit the number of topics created, and resulting in more than the 5 original topics in the data. It will allow for more interesting visualizations.

Getting ready

We will use the BERTopic package to create the visualization. It is available in the poetry environment.

How to do it...

  1. Import the necessary packages and functions:
    import pandas as pd
    import numpy as np
    from bertopic import BERTopic
    from bertopic.representation import KeyBERTInspired
  2. Run the language utilities file:
    %run -i "../util/lang_utils.ipynb"
  3. Read in the data:
    bbc_df = pd.read_csv("../data/bbc-text...
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Python Natural Language Processing Cookbook
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