| Description |
1 online resource (1 volume) : illustrations |
| Series |
Community experience distilled |
|
Community experience distilled.
|
| Contents |
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 |
|
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) |
|
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 |
|
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 |
| Summary |
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 |
| Notes |
Includes index |
|
English |
|
Online resource; title from cover page (Safari, viewed October 12, 2015) |
| Subject |
Data mining.
|
|
R (Computer program language)
|
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Social media.
|
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social media.
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|
COMPUTERS -- General.
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Data mining
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R (Computer program language)
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Social media
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| Form |
Electronic book
|
| Author |
Garg, Vikram, author.
|
| ISBN |
9781784399672 |
|
1784399671 |
|
