Book Image

The Natural Language Processing Workshop

By : Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning
5 (1)
Book Image

The Natural Language Processing Workshop

5 (1)
By: Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning

Overview of this book

Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you’ve never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you’ll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.
Table of Contents (10 chapters)
Preface

7. Text Generation and Summarization

Activity 7.01: Summarizing Complaints in the Consumer Financial Protection Bureau Dataset

Solution

Follow these steps to complete this activity:

  1. Open a Jupyter Notebook and insert a new cell. Add the following code to import the required libraries:
    import warnings
    warnings.filterwarnings('ignore')
    import os
    import csv
    import pandas as pd
    from gensim.summarization import summarize
  2. Insert a new cell and add the following code to fetch the Consumer Complaints dataset and consider the rows that have a complaint narrative. Drop all the columns other than Product, Sub-product, Issue, Sub-issue, and Consumer complaint narrative:
    complaints_pathname = '../data/consumercomplaints/'\
                          'Consumer_Complaints.csv'
    df_all_complaints = pd.read_csv(complaints_pathname)
    df_all_narr = df_all_complaints...