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00000cam a2200000Ii 4500 |
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EBSCO_ocn924210506 |
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OCoLC |
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20231017213018.0 |
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m o d |
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cr unu|||||||| |
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151014s2015 enka o 001 0 eng d |
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|a UMI
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|d WYU
|d UAB
|d RDF
|d OCLCQ
|d OCLCO
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|d QGK
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|a 922456518
|a 922702559
|a 1259197818
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|a 9781784399672
|q (electronic bk.)
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|a 1784399671
|q (electronic bk.)
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|z 1784399671
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|z 9781784396312
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|a (OCoLC)924210506
|z (OCoLC)922456518
|z (OCoLC)922702559
|z (OCoLC)1259197818
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037 |
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|a CL0500000659
|b Safari Books Online
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|a QA76.9.D343
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|a COM
|x 000000
|2 bisacsh
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0 |
4 |
|a 006.3/12
|2 23
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049 |
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|a UAMI
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100 |
1 |
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|a Ravindran, Sharan Kumar,
|e author.
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245 |
1 |
0 |
|a Mastering social media mining with R :
|b extract valuable data from social media sites and make better business decisions using R /
|c Sharan Kumar Ravindran, Vikram Garg.
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246 |
3 |
0 |
|a Extract valuable data from social media sites and make better business decisions using R
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264 |
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1 |
|a Birmingham, UK :
|b Packt Publishing,
|c 2015.
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300 |
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|a 1 online resource (1 volume) :
|b illustrations
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336 |
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|a text
|b txt
|2 rdacontent
|
337 |
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|a computer
|b c
|2 rdamedia
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338 |
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|a online resource
|b cr
|2 rdacarrier
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490 |
1 |
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|a Community experience distilled
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588 |
0 |
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|a Online resource; title from cover page (Safari, viewed October 12, 2015).
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500 |
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|a Includes index.
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505 |
0 |
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|a Cover ; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Fundamentals of Mining; Social media and its importance; Various social media platforms; Social media mining; Challenges for social media mining; Social media mining techniques; Graph mining; Text mining; The generic process of social media mining; Getting authentication from the social website -- OAuth 2.0; Differences between OAuth and OAuth 2.0; Data visualization R packages; The simple word cloud; Sentiment analysis Wordcloud; Preprocessing and cleaning in R
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505 |
8 |
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|a Data modeling -- the application of mining algorithmsOpinion mining (sentiment analysis); Steps for sentiment analysis; Community detection via clustering ; Result visualization; An example of social media mining; Summary; Chapter 2: Mining Opinions, Exploring Trends, and More with Twitter ; Twitter and its importance; Understanding Twitter's APIs; Twitter vocabulary; Creating a Twitter API connection; Creating a new app; Finding trending topics; Searching tweets; Twitter sentiment analysis; Collecting tweets as a corpus; Cleaning the corpus; Estimating sentiment (A); Estimating sentiment (B)
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505 |
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|a The order of stories on a user's home pageRecommendations to friends; Reading the output; Other business cases; Summary; Chapter 4: Finding Popular Photos on Instagram ; Creating an app on the Instagram platform; Installation and authentication of the instaR package; Accessing data from R; Searching public media for a specific hashtag; Searching public media from a specific location; Extracting public media of a user; Extracting user profile; Getting followers; Who does the user follow?; Getting comments; Number of times hashtag is used; Building a dataset; User profile; User media
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505 |
8 |
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|a Travel-related mediaWho do they follow?; Popular personalities; Who has the most followers?; Who follows more people?; Who shared most media?; Overall top users; Most viral media; Finding the most popular destination; Locations; Locations with most likes; Locations most talked about; What are people saying about these locations?; Most repeating locations; Clustering the pictures; Recommendations to the users; How to do it; Top three recommendations; Improvements to the recommendation system; Business case; Reference; Summary; Chapter 5: Let's Build Software with GitHub
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520 |
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|a Chapter 3: Find Friends on Facebook ; Creating an app on the Facebook platform; Rfacebook package installation and authentication; Installation; A closer look at how the package works; A basic analysis of your network; Network analysis and visualization; Social network analysis; Degree; Betweenness; Closeness; Cluster; Communities; Getting Facebook page data; Trending topics; Trend analysis; Influencers; Based on a single post; Based on multiple posts; Measuring CTR performance for a page; Spam detection; Implementing a spam detection algorithm
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546 |
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|a English.
|
590 |
|
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
|
650 |
|
0 |
|a Data mining.
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650 |
|
0 |
|a R (Computer program language)
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650 |
|
0 |
|a Social media.
|
650 |
|
6 |
|a Exploration de données (Informatique)
|
650 |
|
6 |
|a R (Langage de programmation)
|
650 |
|
6 |
|a Médias sociaux.
|
650 |
|
7 |
|a social media.
|2 aat
|
650 |
|
7 |
|a COMPUTERS
|x General.
|2 bisacsh
|
650 |
|
7 |
|a Data mining.
|2 fast
|0 (OCoLC)fst00887946
|
650 |
|
7 |
|a R (Computer program language)
|2 fast
|0 (OCoLC)fst01086207
|
650 |
|
7 |
|a Social media.
|2 fast
|0 (OCoLC)fst01741098
|
700 |
1 |
|
|a Garg, Vikram,
|e author.
|
776 |
|
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|z 1-78439-631-1
|
830 |
|
0 |
|a Community experience distilled.
|
856 |
4 |
0 |
|u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1071006
|z Texto completo
|
938 |
|
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|a EBL - Ebook Library
|b EBLB
|n EBL4191249
|
938 |
|
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|a EBSCOhost
|b EBSC
|n 1071006
|
938 |
|
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|a ProQuest MyiLibrary Digital eBook Collection
|b IDEB
|n cis32777838
|
938 |
|
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|a YBP Library Services
|b YANK
|n 12618689
|
994 |
|
|
|a 92
|b IZTAP
|