Recipes for Mining Twitter Distilling Rich Information from Messy Data Millions of public Twitter streams harbor a wealth of data and once you mine them you can gain some valuable insights This short and concise book offers a collection of recipes to help you extract n

  • Title: 21 Recipes for Mining Twitter: Distilling Rich Information from Messy Data
  • Author: Matthew A. Russell
  • ISBN: null
  • Page: 487
  • Format: Kindle Edition
  • Millions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy to learn Python tools Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit your paMillions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy to learn Python tools Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit your particular needs The recipes include techniques to Use OAuth to access Twitter dataCreate and analyze graphs of retweet relationshipsUse the streaming API to harvest tweets in realtimeHarvest and analyze friends and followersDiscover friendship cliquesSummarize webpages from short URLsThis book is a perfect companion to O Reilly s Mining the Social Web.

    • [PDF] Download ✓ 21 Recipes for Mining Twitter: Distilling Rich Information from Messy Data | by ↠ Matthew A. Russell
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      Posted by:Matthew A. Russell
      Published :2018-09-16T21:35:00+00:00

    One thought on “21 Recipes for Mining Twitter: Distilling Rich Information from Messy Data”

    1. There’s a lot of very useful information on Twitter. Not only information related to your area of expertise and a window into what your customers need and want from your products but also important details about who your potential customers are as well as trends, patterns, and cycles that may directly or indirectly affect your business. There are two main issues when using Twitter for business: volume and organization. Essentially, how can you quickly and efficiently sift through thousands of [...]

    2. A collection of recipes for getting data from Twitter using "twitter" package, a Python wrapper for Twitter API. The nice thing about the recipes is that they are not just "mining" the data, but also using them to do some sort of analysis with Python packages such as NetworkX, NLTK, etc. Although not in detail, the book also mentions some technologies to store/process data in an efficient manner (e.g NoSQL databases).Although the book has a GitHub repository for up-to-date recipes, I am not sure [...]

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