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Mastering Social Media Mining with Python

You're reading from   Mastering Social Media Mining with Python Unearth deeper insight from your social media data with advanced Python techniques for acquisition and analysis

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781783552016
Length 338 pages
Edition 1st Edition
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Author (1):
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Marco Bonzanini Marco Bonzanini
Author Profile Icon Marco Bonzanini
Marco Bonzanini
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Table of Contents (10) Chapters Close

Preface 1. Social Media, Social Data, and Python FREE CHAPTER 2. #MiningTwitter – Hashtags, Topics, and Time Series 3. Users, Followers, and Communities on Twitter 4. Posts, Pages, and User Interactions on Facebook 5. Topic Analysis on Google+ 6. Questions and Answers on Stack Exchange 7. Blogs, RSS, Wikipedia, and Natural Language Processing 8. Mining All the Data! 9. Linked Data and the Semantic Web

Questions and answers

Looking for answers for a particular information need is one of the main uses of the Web. Over time, technology has evolved and Internet users have been changing their online behaviors.

The way people look for information on the Internet nowadays is quite different from 15-20 years ago. Back in the early days, looking for answers mainly meant using a search engine. A study on query logs from a popular search engine in the late 90s (Searching the Web: The Public and Their Queries by Amanda Spink and others, 2001) has shown that in those days, a typical search was very short (on average, 2.4 terms).

In recent years, we started experiencing a transition from short keyword-based search queries to longer conversational queries (or should we say, questions). In other words, search engines have been moving from keyword matching to Natural Language Processing (NLP). For example, Figure 6.1 shows how Google tries to autocomplete a query/questions from the user. The system...

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