Social Media Mining with R.
A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world. Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experienc…
Read More ↓Title Statements
- Full title :Social Media Mining with R.
Summary
A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world. Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.
Read More ↓Edition Statements
- Publication Date:2014.
Publication Statements
- Publisher(s):Packt Publishing
- Place of Publication:Birmingham
- Language:eng
- Publication Date:2014.
The Physical Object
- Description:1 online resource (122 pages)
Edition Identifiers
- ISBN:1783281774, 9781783281770, 1306545528, 9781306545525
- oclc:ocn994494385
- LC Classification:QA76.9 .D343
- Dewey Decimal:006.3/12
Table of Contents
Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Going Viral; Social media mining using sentiment analysis; The state of communication; What is Big Data?; Human sensors and honest signals; Quantitative approaches; Summary; Chapter 2: Getting Started with R; Why R?; Quick start; The basics -- assignment and arithmetic; Functions, arguments, and help; Vectors, sequences, and combining vectors; A quick example -- creating data frames and importing files; Visualization in R; Style and workflow; Additional resources; Summary.
Chapter 3: Mining Twitter with RWhy Twitter data?; Obtaining Twitter data; Preliminary analyses; Summary; Chapter 4: Potentials and Pitfalls of Social Media Data; Opinion mining made difficult; Sentiment and its measurement; The nature of social media data; Traditional versus nontraditional social data; Measurement and inferential challenges; Summary; Chapter 5: Social Media Mining -- Fundamentals; Key concepts of social media mining; Good data versus bad data; Understanding sentiments; Scherer's typology of emotions; Sentiment polarity -- data and classification.
Supervised social media …
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