Mining the Social Web
By: Matthew A Russell
Contributor(s): Klassen MikhailMaterial type: 
TextLanguage: English Publisher: New Delhi Shroff Publishers and distributors 2019Edition: 3rd edDescription: xxiv,399p. PB 23x18cmISBN: 9789352137695Subject(s): Computer programmingDDC classification: 005.8 Summary: All Indian reprints of O'Reilly are printed in grayscale.mine the rich data tucked away in popular social web sites such as Twitter, face book, linked in, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they're talking about, and where they're located—using Python code examples, Jupyter notebooks, or Docker containers.<Br> In part one, each stand alone. Chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blog and feeds, mailboxes, GitHub, and a newly added br>Chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter. <Br> <beget a straightforward synopsis of the social web landscape
Use Docker to easily run each chapter example code, packaged as a Jupiter notebookAdapt and contribute to the code’s open source GitHub repositoryLearn how to employ best-in-class Python 3 tools to slice and Dice the data you collectApply advanced mining techniques such as tfidf, cosine similarity, collocation analysis, clique detection, and image recognition
Build beautiful data visualisations with Python and JavaScript toolkit </br>.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Book
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St Aloysius College PG Library | MAJMC | 005.8 RUSM (Browse shelf) | Available | PG024144 |
All Indian reprints of O'Reilly are printed in grayscale.mine the rich data tucked away in popular social web sites such as Twitter, face book, linked in, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they're talking about, and where they're located—using Python code examples, Jupyter notebooks, or Docker containers.<Br> In part one, each stand alone. Chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blog and feeds, mailboxes, GitHub, and a newly added br>Chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter. <Br> <beget a straightforward synopsis of the social web landscape
Use Docker to easily run each chapter example code, packaged as a Jupiter notebookAdapt and contribute to the code’s open source GitHub repositoryLearn how to employ best-in-class Python 3 tools to slice and Dice the data you collectApply advanced mining techniques such as tfidf, cosine similarity, collocation analysis, clique detection, and image recognition
Build beautiful data visualisations with Python and JavaScript toolkit </br>.
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