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E-book

Learning Social Media Analytics with R.

Bali, Raghav. (Author). Sarkar, Dipanjan. (Added Author). Sharma, Tushar. (Added Author).

Record details

  • ISBN: 9781787125469
  • Physical Description: 1 online resource (394 pages)
  • Edition: 1st ed.
  • Publisher: Birmingham : Packt Publishing, 2017.

Content descriptions

Formatted Contents Note:
Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started with R and Social Media Analytics -- Understanding social media -- Advantages and significance -- Disadvantages and pitfalls -- Social media analytics -- A typical social media analytics workflow -- Data access -- Data processing and normalization -- Data analysis -- Insights -- Opportunities -- Challenges -- Getting started with R -- Environment setup -- Data types -- Data structures -- Vectors -- Arrays -- Matrices -- Lists -- DataFrames -- Functions -- Built-in functions -- User-defined functions -- Controlling code flow -- Looping constructs -- Conditional constructs -- Advanced operations -- apply -- lapply -- sapply -- tapply -- mapply -- Visualizing data -- Next steps -- Getting help -- Managing packages -- Data analytics -- Analytics workflow -- Machine learning -- Machine learning techniques -- Supervised learning -- Unsupervised learning -- Text analytics -- Summary -- Chapter 2: Twitter - What's Happening with 140 Characters -- Understanding Twitter -- APIs -- Registering an application -- Connecting to Twitter using R -- Extracting sample Tweets -- Revisiting analytics workflow -- Trend analysis -- Sentiment analysis -- Key concepts of sentiment analysis -- Subjectivity -- Sentiment polarity -- Opinion summarization -- Features -- Sentiment analysis in R -- Follower graph analysis -- Challenges -- Summary -- Chapter 3: Analyzing Social Networks and Brand Engagements with Facebook -- Accessing Facebook data -- Understanding the Graph API -- Understanding Rfacebook -- Understanding Netvizz -- Data access challenges -- Analyzing your personal social network -- Basic descriptive statistics -- Analyzing mutual interests -- Build your friend network graph.
Visualizing your friend network graph -- Analyzing node properties -- Degree -- Closeness -- Betweenness -- Analyzing network communities -- Cliques -- Communities -- Analyzing an English football social network -- Basic descriptive statistics -- Visualizing the network -- Analyzing network properties -- Diameter -- Page distances -- Density -- Transitivity -- Coreness -- Analyzing node properties -- Degree -- Closeness -- Betweenness -- Visualizing correlation among centrality measures -- Eigenvector centrality -- PageRank -- HITS authority score -- Page neighbours -- Analyzing network communities -- Cliques -- Communities -- Analyzing English Football Club's brand page engagements -- Getting the data -- Curating the data -- Visualizing post counts per page -- Visualizing post counts by post type per page -- Visualizing average likes by post type per page -- Visualizing average shares by post type per page -- Visualizing page engagement over time -- Visualizing user engagement with page over time -- Trending posts by user likes per page -- Trending posts by user shares per page -- Top influential users on popular page posts -- Summary -- Chapter 4: Foursquare - Are You Checked in Yet? -- Foursquare - the app and data -- Foursquare APIs - show me the data -- Creating an application - let me in -- Data access - the twist in the story -- Handling JSON in R - the hidden art -- Getting category data - introduction to JSON parsing and data extraction -- Revisiting the analytics workflow -- Category trend analysis -- Getting the data - the usual hurdle -- The required end point -- Getting data for a city - geometry to the rescue -- Analysis - the fun part -- Basic descriptive statistics - the usual -- Recommendation engine - let's open a restaurant -- Recommendation engine - the clich├ęs -- Framing the recommendation problem.
Building our restaurant recommender -- The sentimental rankings -- Extracting tips data - the go to step -- The actual data -- Analysis of tips -- Basic descriptive statistics -- The final rankings -- Venue graph - where do people go next? -- Challenges for Foursquare data analysis -- Summary -- Chapter 5: Analyzing Software Collaboration Trends I - Social Coding with GitHub -- Environment setup -- Understanding GitHub -- Accessing GitHub data -- Using the rgithub package for data access -- Registering an application on GitHub -- Accessing data using the GitHub API -- Analyzing repository activity -- Analyzing weekly commit frequency -- Analyzing commit frequency distribution versus day of the week -- Analyzing daily commit frequency -- Analyzing weekly commit frequency comparison -- Analyzing weekly code modification history -- Retrieving trending repositories -- Analyzing repository trends -- Analyzing trending repositories created over time -- Analyzing trending repositories updated over time -- Analyzing repository metrics -- Visualizing repository metric distributions -- Analyzing repository metric correlations -- Analyzing relationship between stargazer and repository counts -- Analyzing relationship between stargazer and fork counts -- Analyzing relationship between total forks, repository count, and health -- Analyzing language trends -- Visualizing top trending languages -- Visualizing top trending languages over time -- Analyzing languages with the most open issues -- Analyzing languages with the most open issues over time -- Analyzing languages with the most helpful repositories -- Analyzing languages with the highest popularity score -- Analyzing language correlations -- Analyzing user trends -- Visualizing top contributing users -- Analyzing user activity metrics -- Summary.
Chapter 6: Analyzing Software Collaboration Trends II - Answering Your Questions with StackExchange -- Understanding StackExchange -- Data access -- The StackExchange data dump -- Accessing data dumps -- Contents of data dumps -- Quick overview of the data in data dumps -- Getting started with data dumps -- Data Science and StackExchange -- Demographics and data science -- Challenges -- Summary -- Chapter 7: Believe What You See - Flickr Data Analysis -- A Flickr-ing world -- Accessing Flickr's data -- Creating the Flickr app -- Connecting to R -- Getting started with Flickr data -- Understanding Flickr data -- Understanding more about EXIF -- Understanding interestingness - similarities -- Finding K -- Elbow method -- Silhouette method -- Are your photos interesting? -- Preparing the data -- Building the classifier -- Challenges -- Summary -- Chapter 8: News - The Collective Social Media! -- News data - news is everywhere -- Accessing news data -- Creating applications for data access -- Data extraction - not just an API call -- The API call and JSON monster -- Sentiment trend analysis -- Getting the data - not again -- Basic descriptive statistics - the usual -- Numerical sentiment trends -- Emotion-based sentiment trends -- Topic modeling -- Getting to the data -- Basic descriptive analysis -- Topic modeling for Mr. Trump's phases -- Cleaning the data -- Pre-processing the data -- The modeling part -- Analysis of topics -- Summarizing news articles -- Document summarization -- Understanding LexRank -- Summarizing articles with lexRankr -- Challenges to news data analysis -- Summary -- Index.
Source of Description Note:
Description based on publisher supplied metadata and other sources.
Genre: Electronic books.

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