Book Image

Natural Language Processing Fundamentals

By : Sohom Ghosh, Dwight Gunning
Book Image

Natural Language Processing Fundamentals

By: Sohom Ghosh, Dwight Gunning

Overview of this book

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
Table of Contents (10 chapters)

4. Collecting Text Data from the Web

Activity 6: Extracting Information from an Online HTML Page


Let's extract the data from an online source and analyze it. Follow these steps to implement this activity:

  1. Open a Jupyter notebook.
  2. Import the requests and BeautifulSoup libraries. Pass the URL to requests with the following command. Convert the fetched content into HTML format using BeautifulSoup's HTML parser. Add the following code to do this:
    import requests
    from bs4 import BeautifulSoup
    r = requests.get('')
    soup = BeautifulSoup(r.text, 'html.parser')
  3. To extract the list of headings, look for the h3 tag. Here, we only need the first six headings. We will look for a span tag that has a class attribute with the following set of commands:
    for ele in soup.find_all('h3')[:6]:
        tx = BeautifulSoup(str(ele),'html.parser').find('span&apos...