Social media analytics : techniques and insights for extracting business value out of social media / Matthew Ganis, Avinash Kohirkar.
Material type:
TextLanguage: English Publisher: New Delhi : Pearson, IBM Press, 2017Description: xxxv, 268 pages : illustrations ; 23 cmISBN: 9780133892567; 0133892565Subject(s): Social media -- Research | Data mining | Business -- Data processing | Web usage mining | Social media | Web usage miningDDC classification: 302.23 | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Books | Institute of Public Enterprise, Library S Campus | 302.23 GAN (Browse shelf) | Available | 46171 |
Includes index.
Includes bibliographical references and index.
Foreword --
Preface: mining for gold (or digging in the mud). Just what do we mean when we say social media? ; Why look at this data? ; How does this translate into business value? ; The book's approach ; Why you should read this book ; What this book does and does not focus on --
Acknowledgments --
About the authors --
Part I. Data identification. Looking for data in all the right places. What data do we mean? ; What subset of content are we interested in? ; Whose comments are we interested in? ; What window of time are we interested in? ; Attributes of data that need to be considered ; Summary --
Separating the wheat from the chaff. It all starts with data ; Casting a net ; Regular expressions ; A few words of caution ; It's not what you say but WHERE you say it ; Summary --
Whose comments are we interested in? Looking for the right subset of people ; Are they happy or unhappy? ; Location and language ; Age and gender ; Eminence, prestige, or popularity ; Summary --
Timing is everything. Predictive versus descriptive ; Sentiment ; Time as your friend ; Summary --
Social data: where and why. Structured data versus unstructured data ; Big data ; Paradox of choice: sifting through big data ; Identifying data in social media outlets ; Summary --
Part II. Data analysis. The right tool for the right job. The four dimensions of analysis taxonomy ; Depth of analysis ; Machine capacity ; Domain of analysis ; Velocity of data ; Summary --
Reading tea leaves: discovering themes, topics, or trends. Validating the hypothesis ; Discovering themes and topics ; Using iterative methods ; Summary --
Fishing in a fast-flowing river. Is there value in real time? ; Real time versus near real time ; Forewarned is forearmed ; Stream computing ; IBM InfoSphere streams ; SPL applications ; Directed graphs ; Streams example: SSM ; Value derived from a conference using real-time analytics ; Summary --
If you don't know what you want, you just may find it!: ad hoc exploration. Ad hoc analysis ; An example of ad hoc analysis ; Data integrity ; Summary --
Rivers run deep: deep analysis. Responding to leads identified in social media ; Support for deep analysis in analytics software ; Summary --
The enterprise social network. Social is much more than just collaboration ; Enterprise social network is the memory of the organization ; Understanding the enterprise graph ; Personal social dashboard: details of implementation ; What's next for the enterprise graph? ; Summary --
Part III. Information interpretation. Murphy was right! The art of what could go wrong. Recap: the social analytics process ; Finding the right data ; Communicating clearly ; Choosing filter words carefully ; Understanding that sometimes less is more ; Customizing and modifying tools ; Using the right tool for the right job ; Analyzing consumer reaction during Hurricane Sandy ; Summary --
Visualization as an aid to analytics. Common visualizations ; Common pitfalls ; Visually representing unstructured data ; Summary --
Appendices. Appendix A. Case study. Introduction to the case study: IBMAmplify ; Data identification ; Data analysis ; Information interpretation ; Conclusions ; Index.
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