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Natural Language Processing Fundamentals

You're reading from   Natural Language Processing Fundamentals Build intelligent applications that can interpret the human language to deliver impactful results

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789954043
Length 374 pages
Edition 1st Edition
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Authors (2):
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Dwight Gunning Dwight Gunning
Author Profile Icon Dwight Gunning
Dwight Gunning
Sohom Ghosh Sohom Ghosh
Author Profile Icon Sohom Ghosh
Sohom Ghosh
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Toc

Table of Contents (10) Chapters Close

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

5. Topic Modeling

Activity 10: Topic Modelling Jeopardy Questions

Solution

Let's perform topic modeling on the dataset of Jeopardy questions. Follow these steps to implement this activity:

  1. Open a Jupyter notebook.
  2. Insert a new cell and add the following code to import the pandas library:
    import pandas as pd
    pd.set_option('display.max_colwidth', 800)
  3. To 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)
  4. The data in the DataFrame is not clean. In order to clean it, we remove records that have missing values in the Question column. Add the following code to do this:
    questions = questions.dropna(subset=['Question'])
  5. Now import the gensim preprocessing utility and use it to preprocess the questions further. Add the following code to do this:
    from gensim.parsing.preprocessing import preprocess_string...
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