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The Natural Language Processing Workshop

You're reading from   The Natural Language Processing Workshop Confidently design and build your own NLP projects with this easy-to-understand practical guide

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800208421
Length 452 pages
Edition 1st Edition
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Authors (6):
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Sohom Ghosh Sohom Ghosh
Author Profile Icon Sohom Ghosh
Sohom Ghosh
Nipun Sadvilkar Nipun Sadvilkar
Author Profile Icon Nipun Sadvilkar
Nipun Sadvilkar
Rohan Chopra Rohan Chopra
Author Profile Icon Rohan Chopra
Rohan Chopra
Muzaffar Bashir Shah Muzaffar Bashir Shah
Author Profile Icon Muzaffar Bashir Shah
Muzaffar Bashir Shah
Dwight Gunning Dwight Gunning
Author Profile Icon Dwight Gunning
Dwight Gunning
Aniruddha M. Godbole Aniruddha M. Godbole
Author Profile Icon Aniruddha M. Godbole
Aniruddha M. Godbole
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Toc

Table of Contents (10) Chapters Close

Preface
1. Introduction to Natural Language Processing 2. Feature Extraction Methods FREE CHAPTER 3. Developing a Text Classifier 4. Collecting Text Data with Web Scraping and APIs 5. Topic Modeling 6. Vector Representation 7. Text Generation and Summarization 8. Sentiment Analysis Appendix

5. Topic Modeling

Activity 5.01: Topic-Modeling Jeopardy Questions

Solution

Let's perform topic modeling on the dataset of Jeopardy questions:

  1. Open a Jupyter Notebook.
  2. Insert a new cell and add the following code to import pandas and other libraries:
    import numpy as np
    import spacy
    nlp = spacy.load('en_core_web_sm')
    import pandas as pd
    pd.set_option('display.max_colwidth', 800)
  3. After downloading the data, you can extract it and place at the location below. Then load the Jeopardy CSV file into a pandas DataFrame. Insert a new cell and add the following code:
    JEOPARDY_CSV =  '../data/jeopardy/Jeopardy.csv'
    questions = pd.read_csv(JEOPARDY_CSV)
    questions.columns = [x.strip() for x in questions.columns]
  4. The data in the DataFrame is not clean. In order to clean it, remove records that have missing values in the Question column. Add the following code to do this:
    questions = questions.dropna(subset=['Question'])
  5. Find...
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