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Social Media Data Mining and Analytics.
Harness the power of social media to predict customer behavior and improve sales Social media is the biggest source of Big Data. Because of this, 90% of Fortune 500 companies are investing in Big Data initiatives that will help them predict consumer behavior to produce better sales results. Written...
| Call Number: | Libro Electrónico |
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| Main Author: | |
| Other Authors: | |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
Somerset :
John Wiley & Sons, Incorporated,
2018.
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| Subjects: | |
| Online Access: | Texto completo |
Table of Contents:
- Cover; Title Page; Copyright; Contents; Introduction; Human Interactions Measured; Online Behavior Through Data Collection; What Types of Data Are Essential to Collect?; Asking and Answering Questions with Data; The Datasets Used in This Book; Wikipedia; Twitter; Stack Exchange; LiveJournal; Scientific Documents from Cora; Amazon Fine Food Reviews; MovieLens Movie Ratings; The Languages and Frameworks Used in This Book; Python; Scalding; System Requirements to Run the Examples; Overview of the Chapters; Online Repository for the Book; Chapter 1 Users: The Who of Social Media.
- Measuring Variations in User Behavior in WikipediaThe Diversity of User Activities; The Origin of the User Activity Distribution; The Consequences of the Power Law; The Long Tail in Human Activities; Long Tails Everywhere: The 80/20 Rule (p/q Rule); Online Behavior on Twitter; Retrieving Tweets for Users; Logarithmic Binning; User Activities on Twitter; Summary; Chapter 2 Networks: The How of Social Media; Types and Properties of Social Networks; When Users Create the Connections: Explicit Networks; Directed Versus Undirected Graphs; Node and Edge Properties; Weighted Graphs.
- Creating Graphs from Activities: Implicit NetworksVisualizing Networks; Degrees: The Winner Takes All; Counting the Number of Connections; The Long Tail in User Connections; Beyond the Idealized Network Model; Capturing Correlations: Triangles, Clustering, and Assortativity; Local Triangles and Clustering; Assortativity; Summary; Chapter 3 Temporal Processes: The When of Social Media; What Traditional Models Tell You About Events in Time; When Events Happen Uniformly in Time; Inter-Event Times; Comparing to a Memoryless Process; Autocorrelations; Deviations from Memorylessness.
- Periodicities in Time in User ActivitiesBursty Activities of Individuals; Correlations and Bursts; Reservoir Sampling; Forecasting Metrics in Time; Finding Trends; Finding Seasonality; Forecasting Time Series with ARIMA; The Autoregressive Part ("AR"); The Moving Average Part ("MA"); The Full ARIMA(p, d, q) Model; Summary; Chapter 4 Content: The What of Social Media; Defining Content: Focus on Text and Unstructured Data; Creating Features from Text: The Basics of Natural Language Processing; The Basic Statistics of Term Occurrences in Text; Using Content Features to Identify Topics.
- The Popularity of TopicsHow Diverse Are Individual Users' Interests?; Extracting Low-Dimensional Information from High-Dimensional Text; Topic Modeling; Unsupervised Topic Modeling; Supervised Topic Modeling; Relational Topic Modeling; Summary; Chapter 5 Processing Large Datasets; MapReduce: Structuring Parallel and Sequential Operations; Counting Words; Skew: The Curse of the Last Reducer; Multi-Stage MapReduce Flows; Fan-Out; Merging Data Streams; Joining Two Data Sources; Joining Against Small Datasets; Models of Large-Scale MapReduce; Patterns in MapReduce Programming.