Social media mining with R : deploy cutting-edge sentiment analysis techniques to real-world social media data using 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 wi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Danneman, Nathan (Autor), Heimann, Richard, 1979- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2014.
Colección:Community experience distilled.
Temas:
Data mining.
Social media.
Data Mining
Social Media
Exploration de données (Informatique)
Médias sociaux.
social media.
COMPUTERS > General.
Data mining
Social media
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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 mining
  • lexicon-based sentiment Supervised social media mining
  • Naive Bayes classifiers; Unsupervised social media mining
  • Item Response Theory for text scaling; Summary; Chapter 6: Social Media Mining
  • Case Studies; Introductory considerations; Case study 1
  • supervised social media mining
  • lexicon-based sentiment; Case study 2
  • Naive Bayes classifier; Case study 3
  • IRT models for unsupervised sentiment scaling; Summary; Appendix: Conclusions and Next Steps; Final thoughts; An expanding field; Further reading; Bibliography; Index

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